diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 00000000..70feec2c --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,29 @@ +name: Docs + +on: + pull_request: + workflow_dispatch: + +jobs: + docs: + runs-on: ubuntu-latest + + name: Docs + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Fetch all tags and branches + run: git fetch --prune --unshallow + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.10' + + - name: Install dependencies + run: pip install .[dev] && pip install -r docs/requirements.txt + + - name: Generate Sphinx HTML + run: cd docs && make html \ No newline at end of file diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ac040fc1..5f42f4dc 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -19,10 +19,10 @@ jobs: python-version: "3.10" - name: Install release packages - run: pip install setuptools wheel twine setuptools-scm[toml] + run: pip install build twine - name: Build distribution - run: python setup.py sdist bdist_wheel + run: python -m build - name: Publish to Test PyPi env: diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 00000000..47b44e5a --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,29 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.12" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + fail_on_warning: true + +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - requirements: docs/requirements.txt \ No newline at end of file diff --git a/README.md b/README.md index 827c2633..711b666d 100644 --- a/README.md +++ b/README.md @@ -29,6 +29,7 @@ Resources to get started: * [RDF Primer](https://www.w3.org/TR/rdf11-concepts/) * [RDFLib (Python)](https://pypi.org/project/rdflib/) * [One Example for Modeling RDF as ArangoDB Graphs](https://www.arangodb.com/docs/stable/data-modeling-graphs-from-rdf.html) + ## Installation #### Latest Release @@ -41,69 +42,73 @@ pip install git+https://github.com/ArangoDB-Community/ArangoRDF ``` ## Quickstart -Run the full version with Google Colab: Open In Colab +Open In Colab ```py from rdflib import Graph from arango import ArangoClient from arango_rdf import ArangoRDF -db = ArangoClient(hosts="http://localhost:8529").db("_system_", username="root", password="") +db = ArangoClient().db() adbrdf = ArangoRDF(db) -g = Graph() -g.parse("https://raw.githubusercontent.com/stardog-union/stardog-tutorials/master/music/beatles.ttl") - -# RDF to ArangoDB -################################################################################### +def beatles(): + g = Graph() + g.parse("https://raw.githubusercontent.com/ArangoDB-Community/ArangoRDF/main/tests/data/rdf/beatles.ttl", format="ttl") + return g +``` -# 1.1: RDF-Topology Preserving Transformation (RPT) -adbrdf.rdf_to_arangodb_by_rpt("Beatles", g, overwrite_graph=True) +### RDF to ArangoDB -# 1.2: Property Graph Transformation (PGT) -adbrdf.rdf_to_arangodb_by_pgt("Beatles", g, overwrite_graph=True) +**Note**: RDF-to-ArangoDB functionality has been implemented using concepts described in the paper +*[Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches](https://arxiv.org/abs/2210.05781)*. So we offer two transformation approaches: -g = adbrdf.load_meta_ontology(g) +1. [RDF-Topology Preserving Transformation (RPT)](https://arangordf.readthedocs.io/en/docs/rdf_to_arangodb_rpt.html) +2. [Property Graph Transformation (PGT)](https://arangordf.readthedocs.io/en/docs/rdf_to_arangodb_pgt.html) -# 1.3: RPT w/ Graph Contextualization -adbrdf.rdf_to_arangodb_by_rpt("Beatles", g, contextualize_graph=True, overwrite_graph=True) +```py +# 1. RDF-Topology Preserving Transformation (RPT) +adbrdf.rdf_to_arangodb_by_rpt(name="BeatlesRPT", rdf_graph=beatles(), overwrite_graph=True) -# 1.4: PGT w/ Graph Contextualization -adbrdf.rdf_to_arangodb_by_pgt("Beatles", g, contextualize_graph=True, overwrite_graph=True) +# 2. Property Graph Transformation (PGT) +adbrdf.rdf_to_arangodb_by_pgt(name="BeatlesPGT", rdf_graph=beatles(), overwrite_graph=True) +``` -# 1.5: PGT w/ ArangoDB Document-to-Collection Mapping Exposed -adb_mapping = adbrdf.build_adb_mapping_for_pgt(g) -print(adb_mapping.serialize()) -adbrdf.rdf_to_arangodb_by_pgt("Beatles", g, adb_mapping, contextualize_graph=True, overwrite_graph=True) +### ArangoDB to RDF -# ArangoDB to RDF -################################################################################### +```py +# pip install arango-datasets +from arango_datasets import Datasets -# Start from scratch! -g = Graph() -g.parse("https://raw.githubusercontent.com/stardog-union/stardog-tutorials/master/music/beatles.ttl") -adbrdf.rdf_to_arangodb_by_pgt("Beatles", g, overwrite_graph=True) +name = "OPEN_INTELLIGENCE_ANGOLA" +Datasets(db).load(name) -# 2.1: Via Graph Name -g2, adb_mapping_2 = adbrdf.arangodb_graph_to_rdf("Beatles", Graph()) +# 1. Graph to RDF +rdf_graph = adbrdf.arangodb_graph_to_rdf(name, rdf_graph=Graph()) -# 2.2: Via Collection Names -g3, adb_mapping_3 = adbrdf.arangodb_collections_to_rdf( - "Beatles", - Graph(), - v_cols={"Album", "Band", "Class", "Property", "SoloArtist", "Song"}, - e_cols={"artist", "member", "track", "type", "writer"}, +# 2. Collections to RDF +rdf_graph_2 = adbrdf.arangodb_collections_to_rdf( + name, + rdf_graph=Graph(), + v_cols={"Event", "Actor", "Source"}, + e_cols={"eventActor", "hasSource"}, ) -print(len(g2), len(adb_mapping_2)) -print(len(g3), len(adb_mapping_3)) - -print('--------------------') -print(g2.serialize()) -print('--------------------') -print(adb_mapping_2.serialize()) -print('--------------------') +# 3. Metagraph to RDF +rdf_graph_3 = adbrdf.arangodb_to_rdf( + name=name, + rdf_graph=Graph(), + metagraph={ + "vertexCollections": { + "Event": {"date", "description", "fatalities"}, + "Actor": {"name"} + }, + "edgeCollections": { + "eventActor": {} + }, + }, +) ``` ## Development & Testing @@ -123,76 +128,3 @@ def pytest_addoption(parser): parser.addoption("--username", action="store", default="root") parser.addoption("--password", action="store", default="") ``` - -## Additional Info: RDF to ArangoDB - -RDF-to-ArangoDB functionality has been implemented using concepts described in the paper *[Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches](https://arxiv.org/abs/2210.05781)*. - -In other words, `ArangoRDF` offers 2 RDF-to-ArangoDB transformation methods: -1. RDF-topology Preserving Transformation (RPT): `ArangoRDF.rdf_to_arangodb_by_rpt()` -2. Property Graph Transformation (PGT): `ArangoRDF.rdf_to_arangodb_by_pgt()` - -RPT preserves the RDF Graph structure by transforming each RDF Statement into an ArangoDB Edge. - -PGT on the other hand ensures that Datatype Property Statements are mapped as ArangoDB Document Properties. - -```ttl -@prefix ex: . -@prefix xsd: . -ex:book ex:publish_date "1963-03-22"^^xsd:date . -ex:book ex:pages "100"^^xsd:integer . -ex:book ex:cover 20 . -ex:book ex:index 55 . -``` - -| RPT | PGT | -|:-------------------------:|:-------------------------:| -| ![image](https://user-images.githubusercontent.com/43019056/232347662-ab48ebfb-e215-4aff-af28-a5915414a8fd.png) | ![image](https://user-images.githubusercontent.com/43019056/232347681-c899ef09-53c7-44de-861e-6a98d448b473.png) | - --------------------- -### RPT - - -The `ArangoRDF.rdf_to_arangodb_by_rpt` method will store the RDF Resources of your RDF Graph under the following ArangoDB Collections: - - - {graph_name}_URIRef: The Document collection for `rdflib.term.URIRef` resources. - - {graph_name}_BNode: The Document collection for`rdflib.term.BNode` resources. - - {graph_name}_Literal: The Document collection for `rdflib.term.Literal` resources. - - {graph_name}_Statement: The Edge collection for all triples/quads. - --------------------- -### PGT - -In contrast to RPT, the `ArangoRDF.rdf_to_arangodb_by_pgt` method will rely on the nature of the RDF Resource/Statement to determine which ArangoDB Collection it belongs to. This is referred as the **ArangoDB Collection Mapping Process**. This process relies on 2 fundamental URIs: - -1) `` (adb:collection) - - Any RDF Statement of the form ` "Person"` will map the Subject to the ArangoDB "Person" document collection. - -2) `` (rdf:type) - - This strategy is divided into 3 cases: - - 1. If an RDF Resource only has one `rdf:type` statement, - then the local name of the RDF Object is used as the ArangoDB - Document Collection name. For example, - ` ` - would create an JSON Document for ``, - and place it under the `Person` Document Collection. - NOTE: The RDF Object will also have its own JSON Document - created, and will be placed under the "Class" - Document Collection. - - 2. If an RDF Resource has multiple `rdf:type` statements, - with some (or all) of the RDF Objects of those statements - belonging in an `rdfs:subClassOf` Taxonomy, then the - local name of the "most specific" Class within the Taxonomy is - used (i.e the Class with the biggest depth). If there is a - tie between 2+ Classes, then the URIs are alphabetically - sorted & the first one is picked. - - 3. If an RDF Resource has multiple `rdf:type` statements, with none - of the RDF Objects of those statements belonging in an - `rdfs:subClassOf` Taxonomy, then the URIs are - alphabetically sorted & the first one is picked. The local - name of the selected URI will be designated as the Document - collection for that Resource. --------------------- diff --git a/arango_rdf/__init__.py b/arango_rdf/__init__.py index 17da9738..91ccf6d8 100644 --- a/arango_rdf/__init__.py +++ b/arango_rdf/__init__.py @@ -1 +1,2 @@ +from arango_rdf.controller import ArangoRDFController # noqa: F401 from arango_rdf.main import ArangoRDF # noqa: F401 diff --git a/arango_rdf/controller.py b/arango_rdf/controller.py index b2c02a31..55cf7aa1 100644 --- a/arango_rdf/controller.py +++ b/arango_rdf/controller.py @@ -10,9 +10,19 @@ class ArangoRDFController(AbstractArangoRDFController): - """ArangoDB-RDF controller. + """Controller used in RDF-to-ArangoDB (PGT). - You can derive your own custom ArangoRDFController. + Responsible for handling how the ArangoDB Collection Mapping Process + identifies the "ideal RDFS Class" among a selection of RDFS Classes + for a given RDF Resource. + + The "ideal RDFS Class" is defined as an RDFS Class whose local name best + represents the RDF Resource in question. This local name will be + used as the ArangoDB Collection name that will store **rdf_resource**. + + `Read more about how the PGT ArangoDB Collection Mapping + Process works here + <./rdf_to_arangodb_pgt.html#arangodb-collection-mapping-process>`_. """ def __init__(self) -> None: @@ -28,42 +38,19 @@ def identify_best_class( """Find the ideal RDFS Class among a selection of RDFS Classes. Essential for the ArangoDB Collection Mapping Process used in RDF-to-ArangoDB (PGT). - The "ideal RDFS Class" is defined as an RDFS Class whose local name can be - used as the ArangoDB Document Collection that will store **rdf_resource**. + `Read more about how the PGT ArangoDB Collection Mapping + Process works here + <./rdf_to_arangodb_pgt.html#arangodb-collection-mapping-process>`_. + + The "ideal RDFS Class" is defined as an RDFS Class whose local name best + represents the RDF Resource in question. This local name will be + used as the ArangoDB Collection name that will store **rdf_resource**. This system is a work-in-progress. Users are welcome to overwrite this method via their own implementation of the `ArangoRDFController` - Python Class. - - NOTE: Users are able to access the RDF Graph of the current - RDF-to-ArangoDB transformation via the `self.rdf_graph` - instance variable, and the database instance via the - `self.db` instance variable. - - The current identification process goes as follows: - 1) If an RDF Resource only has one `rdf:type` statement - (either by explicit definition or by domain/range inference), - then the local name of the single RDFS Class is used as the ArangoDB - Document Collection name. For example, - - would place the JSON Document for - under the ArangoDB "Person" Document Collection. - - 2) If an RDF Resource has multiple `rdf:type` statements - (either by explicit definition or by domain/range inference), - with some (or all) of the RDFS Classes of those statements - belonging in an `rdfs:subClassOf` Taxonomy, then the - local name of the "most specific" Class within the Taxonomy is - used (i.e the Class with the biggest depth). If there is a - tie between 2+ Classes, then the URIs are alphabetically - sorted & the first one is picked. Relies on **subclass_tree**. - - 3) If an RDF Resource has multiple `rdf:type` statements, with - none of the RDFS Classes of those statements belonging in an - `rdfs:subClassOf` Taxonomy, then the URIs are - alphabetically sorted & the first one is picked. The local - name of the selected URI will be designated as the Document - Collection for **rdf_resource**. + Class. Users are able to access the RDF Graph of the current + RDF-to-ArangoDB transformation via `self.rdf_graph`, and the + database instance via the `self.db`. :param rdf_resource: The RDF Resource in question. :type rdf_resource: URIRef | BNode @@ -73,12 +60,12 @@ def identify_best_class( domain/range inference. :type class_set: Set[str] :param subclass_tree: The Tree data structure representing - the RDFS subClassOf Taxonomy. See `ArangoRDF.__build_subclass_tree()` - for more info. + the RDFS subClassOf Taxonomy. + See :func:`arango_rdf.main.ArangoRDF.__build_subclass_tree` for more info. :type subclass_tree: arango_rdf.utils.Tree - :return: The most suitable RDFS Class URI among the set of RDFS Classes - to use as the ArangoDB Document Collection name associated to - **rdf_resource**. + :return: The string representation of the URI of the most suitable + RDFS Class URI among the set of RDFS Classes to use as the ArangoDB + Document Collection name for **rdf_resource**. :rtype: str """ # These are accessible! diff --git a/arango_rdf/main.py b/arango_rdf/main.py index c9ade6b1..5d25be12 100644 --- a/arango_rdf/main.py +++ b/arango_rdf/main.py @@ -175,20 +175,6 @@ def arangodb_to_rdf( :type rdf_graph: rdflib.graph.Graph :param metagraph: An dictionary of dictionaries defining the ArangoDB Vertex & Edge Collections whose entries will be inserted into the RDF Graph. - For example: - - .. code-block:: python - { - "vertexCollections": { - "Person": {"name", "age"}, - "Book": {"title", "author"} - }, - "edgeCollections": { - "Likes": {"date"}, - "Wrote": {"date"} - } - } - :type metagraph: arango_rdf.typings.ADBMetagraph :param explicit_metagraph: Only keep the document attributes specified in **metagraph** when importing to RDF (is True by default). Otherwise, @@ -558,35 +544,24 @@ def rdf_to_arangodb_by_rpt( This method will store the RDF Resources of **rdf_graph** under the following ArangoDB Collections: - ``` - "f{name}_URIRef" # Vertex collection for `rdflib.term.URIRef`. - "f{name}_BNode" # Vertex collection for`rdflib.term.BNode`. - "f{name}_Literal" # Vertex collection for `rdflib.term.Literal`. - "f{name}_Statement" # Edge collection for all triples/quads. - ``` + 1. ``{Name}_URIRef``: Vertex collection for ``rdflib.term.URIRef`` resources. + 2. ``{Name}_BNode``: Vertex collection for ``rdflib.term.BNode`` resources. + 3. ``{Name}_Literal``: Vertex collection for ``rdflib.term.Literal`` resources. + 4. ``{Name}_Statement``: Edge collection for all triples/quads. :param name: The name of the RDF Graph :type name: str - :param rdf_graph: The RDF Graph object. NOTE: This object - is modified in-place in order for PGT to work. Do not - expect the original state of **rdf_graph** to be preserved. + :param rdf_graph: The RDF Graph object. NOTE: This object is modified + in-place in order for PGT to work. Do not expect the original state of + **rdf_graph** to be preserved. :type: rdf_graph: rdflib.graph.Graph :param contextualize_graph: A work-in-progress flag that seeks to enhance the Terminology Box of **rdf_graph** by providing the following features: - 1) Process RDF Predicates within **rdf_graph** as their own ArangoDB - Document, and cast a (predicate RDF.type RDF.Property) edge - relationship into the ArangoDB graph for every RDF predicate - used in the form (subject predicate object) within **rdf_graph**. - 2) Provide RDFS.Domain & RDFS.Range **Inference** on all - RDF Resources within the **rdf_graph**, so long that no - RDF.Type statement already exists in **rdf_graph** - for the given resource. - 3) Provide RDFS.Domain & RDFS.Range **Introspection** on all - RDF Predicates with the **rdf_graph**, so long that - no RDFS.Domain or RDFS.Range statement already exists - for the given predicate. - 4) TODO - What's next? + + 1) Loading Meta Ontologies (i.e OWL, RDF, RDFS, etc.) into the RDF Graph + 2) Providing Domain & Range Inference + 3) Providing Domain & Range Introspection :type contextualize_graph: bool :param flatten_reified_triples: If set to False, will preserve the RDF structure of reified triples. If set to True, will convert any reified @@ -731,124 +706,23 @@ def rdf_to_arangodb_by_pgt( """Create an ArangoDB Graph from an RDF Graph using the Property Graph Transformation (PGT) Algorithm. - In contrast to RPT, PGT ensures that datatype property statements are - mapped to node properties in the PG. More info on PGT can be found - in the package's README file, or in the following - paper: https://arxiv.org/pdf/2210.05781.pdf. + PGT ensures that datatype property statements (i.e statements whose + objects are Literals) are mapped to document properties in the + Property Graph. `Learn more about PGT here + <./rdf_to_arangodb_pgt.html>`_. - In contrast to RPT, this method will rely on + Contrary to RPT, this method will rely on the nature of the RDF Resource/Statement to determine which ArangoDB - Collection it belongs to. The ArangoDB Collection mapping process relies - on two fundamental URIs: - - 1) (adb:collection) - - Any RDF Statement of the form - "Person" - will map the Subject to the ArangoDB - "Person" document collection. - - 2) (rdf:type) - - This strategy is divided into 3 cases: - 2.1) If an RDF Resource only has one `rdf:type` statement, - then the local name of the RDF Object is used as the ArangoDB - Document Collection name. For example, - - would create an JSON Document for , - and place it under the "Person" Document Collection. - NOTE: The RDF Object will also have its own JSON Document - created, and will be placed under the "Class" - Document Collection. - - 2.2) If an RDF Resource has multiple `rdf:type` statements, - with some (or all) of the RDF Objects of those statements - belonging in an `rdfs:subClassOf` Taxonomy, then the - local name of the "most specific" Class within the Taxonomy is - used (i.e the Class with the biggest depth). If there is a - tie between 2+ Classes, then the URIs are alphabetically - sorted & the first one is picked. - - 2.3) If an RDF Resource has multiple `rdf:type` statements, with - none of the RDF Objects of those statements belonging in an - `rdfs:subClassOf` Taxonomy, then the URIs are - alphabetically sorted & the first one is picked. The local - name of the selected URI will be designated as the Document - collection for that Resource. - - NOTE 1: If **contextualize_graph** is set to True, then additional - `rdf:type` statements may be generated via ArangoRDF's Domain & Range - Inference feature. These "synthetic" statements will be considered when - mapping RDF Resources to the correct ArangoDB Collections, but ONLY if - there were no "original" rdf:type statements to consider for - the given RDF Resource. - - NOTE 2: The ArangoDB Collection Mapping algorithm is a Work in Progress, - and will most likely be subject to change for the time being. - - In contrast to RPT, regardless of whether **contextualize_graph** is set to + Collection it belongs to. This process is referred to as the + ArangoDB Collection Mapping Process. `Learn more about the PGT ArangoDB + Collection Mapping Process here + <./rdf_to_arangodb_pgt.html#arangodb-collection-mapping-process>`_. + + Contrary to RPT, regardless of whether **contextualize_graph** is set to True or not, all RDF Predicates within every RDF Statement in **rdf_graph** will be processed as their own ArangoDB Document, and will be stored under the "Property" Vertex Collection. - To demo the ArangoDB Collection Mapping process, - let us consider the following RDF Graph: - - ``` - @prefix ex: . - @prefix adb: . - @prefix rdfs: . - - ex:B rdfs:subClassOf ex:A . - ex:C rdfs:subClassOf ex:A . - ex:D rdfs:subClassOf ex:C . - - ex:alex rdf:type ex:A . - - ex:sam ex:age 25 . - ex:age rdfs:domain ex:A - - ex:john rdf:type ex:B . - ex:john rdf:type ex:D . - - ex:mike rdf:type ex:G - ex:mike rdf:type ex:F - ex:mike rdf:type ex:E - - ex:frank adb:collection "Z" . - ex:frank rdf:type D . - - ex:bob ex:name "Bob" . - ``` - Given the RDF TTL Snippet above, we can derive the following - ArangoDB Collection mappings: - - ex:alex --> "A" - - This RDF Resource only has one associated `rdf:type` statement. - - ex:sam --> "A" - - Although this RDF Resource has no `rdf:type` associated statement, - we can infer from the domain of the property it uses (ex:age) that - it is of type ex:A. - - ex:john --> "D" - - This RDF Resource has 2 `rdf:type` statements, but `ex:D` is "deeper" - than `ex:B` when considering the `rdfs:subClassOf` Taxonomy. - - ex:mike --> "E" - - This RDF Resource has multiple `rdf:type` statements, with - none belonging to the `rdfs:subClassOf` Taxonomy. - Therefore, Alphabetical Sorting is used. - - ex:frank --> "Z" - - This RDF Resource has an `adb:collection` statement associated - to it, which is prioritized over any other `rdf:type` - statement it may have. - - ex:bob --> "UnknownResource" - - This RDF Resource has neither an `rdf:type` statement - nor an `adb:collection` statement associated to it. It - is therefore placed under the "UnknownResource" - Document Collection. - :param name: The name of the RDF Graph :type name: str :param rdf_graph: The RDF Graph object. NOTE: This object @@ -857,8 +731,8 @@ def rdf_to_arangodb_by_pgt( :type: rdf_graph: rdflib.graph.Graph :param adb_col_statements: An optional RDF Graph containing ArangoDB Collection statements of the form - (adb_vertex http://arangodb/collection "adb_v_col"). - If specified, will be used to determine the ArangoDB Collection + `adb_vertex http://arangodb/collection "adb_v_col" .`. + Useful for creating a custom ArangoDB Collection mapping of RDF Resources within **rdf_graph**. Defaults to None. NOTE: Cannot be used in conjunction with collection statements in **rdf_graph**. @@ -866,43 +740,38 @@ def rdf_to_arangodb_by_pgt( :param write_adb_col_statements: Run the ArangoDB Collection Mapping Process for **rdf_graph** to write the ArangoDB Collection statements of the form - (adb_vertex http://arangodb/collection "adb_v_col") + `adb_vertex http://arangodb/collection "adb_v_col" . ` into **adb_col_statements**. This parameter is ignored if **contextualize_graph** is set to True, as the ArangoDB Collection Mapping Process is required for Graph Contextualization. + See :func:`write_adb_col_statements` for more information. :type write_adb_col_statements: bool :param contextualize_graph: A work-in-progress flag that seeks to enhance the Terminology Box of **rdf_graph** by providing the following features: - 1) Cast a (predicate RDF.type RDF.Property) edge - relationship into the ArangoDB graph for every RDF predicate - used in the form (subject predicate object) within **rdf_graph**. - 2) Provide RDFS.Domain & RDFS.Range Inference on all - RDF Resources within the **rdf_graph**, so long that no - RDF.Type statement already exists in **rdf_graph** - for the given resource. - 3) Provide RDFS.Domain & RDFS.Range Introspection on all - RDF Predicates with the **rdf_graph**, so long that - no RDFS.Domain or RDFS.Range statement already exists - for the given predicate. - 4) TODO - What's next? + + 1) Loading Meta Ontologies (i.e OWL, RDF, RDFS, etc.) into the RDF Graph + 2) Providing Domain & Range Inference + 3) Providing Domain & Range Introspection :type contextualize_graph: bool + :param flatten_reified_triples: If set to False, will preserve the RDF + structure of any Reified Triple. If set to True, will "flatten" any reified + triples into a regular Property Graph Edge. Defaults to True. + + `Learn more about Triple Reification here <./reification.html>`_. + :type flatten_reified_triples: bool :param overwrite_graph: Overwrites the ArangoDB graph identified by **name** if it already exists, and drops its associated collections. Defaults to False. :type overwrite_graph: bool :param batch_size: If specified, runs the ArangoDB Data Ingestion process for every **batch_size** RDF triples/quads within **rdf_graph**. - Defaults to `len(rdf_graph)`. + Defaults to None. :type batch_size: int | None - :param flatten_reified_triples: If set to False, will preserve the RDF - Structure of any reified triples. If set to True, will convert any reified - triples into regular ArangoDB edges. Defaults to True. - :type flatten_reified_triples: bool :param adb_import_kwargs: Keyword arguments to specify additional parameters for the ArangoDB Data Ingestion process. - The full parameter list is here: - https://docs.python-arango.com/en/main/specs.html#arango.collection.Collection.import_bulk + The full parameter list is + `here `_. # noqa: E501 :return: The ArangoDB Graph API wrapper. :rtype: arango.graph.Graph """ @@ -1069,50 +938,19 @@ def write_adb_col_statements( rdf_graph: RDFGraph, adb_col_statements: Optional[RDFGraph] = None, ) -> RDFGraph: - """RDF -> ArangoDB (PGT): Returns an RDF Graph to map RDF Resources to - their respective ArangoDB Collection. + """RDF -> ArangoDB (PGT): Run the ArangoDB Collection Mapping Process for + **rdf_graph** to map RDF Resources to their respective ArangoDB Collection. The PGT Algorithm relies on the ArangoDB Collection Mapping Process to identify the ArangoDB Collection of every RDF Resource. Using this method prior - to running `ArangoRDF.rdf_to_arangodb_by_pgt()` allows you to visualize and - modify the mapping. See the `ArangoRDF.rdf_to_arangodb_by_pgt()` documentation - for an explanation on the ArangoDB Collection Mapping Process. - - A common use case would look like this: - - .. code-block:: python - from rdflib import Graph - from arango_rdf import ArangoRDF - - adbrdf = ArangoRDF(db) - - g = Graph() - g.parse(...) - g.add(...) + to running :func:`rdf_to_arangodb_by_pgt` allows you to visualize and + modify the mapping. `Learn more about the PGT ArangoDB + Collection Mapping Process here + <./rdf_to_arangodb_pgt.html#arangodb-collection-mapping-process>`_. - adb_col_statements = adbrdf.write_adb_col_statements(g) - adb_col_statements.serialize(...) - adb_col_statements.add(...) - adb_col_statements.remove(...) - - adbrdf.rdf_to_arangodb_by_pgt( - 'MyGraph', rdf_graph=g, adb_col_statements=adb_col_statements - ) - - NOTE: Running this method prior to `ArangoRDF.rdf_to_arangodb_by_pgt` + NOTE: Running this method prior to :func:`rdf_to_arangodb_by_pgt` is unnecessary if the user is not interested in - viewing/modifying the ADB Mapping. - - For example, the `adb_col_statements` may look like this: - - .. code-block:: - @prefix adb: . - - adb:collection "Person" . - adb:collection "Person" . - adb:collection "Property" . - adb:collection "Class" . - adb:collection "Dog" . + viewing/modifying the ArangoDB Mapping. NOTE: There can only be 1 `adb:collection` statement associated to each RDF Resource. @@ -1123,10 +961,10 @@ def write_adb_col_statements( `adb:collection` statements. If not provided, a new RDF Graph will be created. Defaults to None. NOTE: The ArangoDB Collection Mapping Process - heavily relies on mapping certain RDF Resources to the - "Class" and "Property" ArangoDB Collections. Therefore + relies heavily on mapping certain RDF Resources to the + `"Class"` and `"Property"` ArangoDB Collections. Therefore, it is currently not possible to overwrite any RDF Resources - that belong to these two collections. + that belong to these collections. :type adb_col_statements: rdflib.graph.Graph | None :type adb_col_statements: Optional[rdflib.graph.Graph] """ diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d4bb2cbb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ 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----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import os +import sys + +sys.path.insert(0, os.path.abspath("..")) + +project = 'ArangoRDF' +copyright = '2024, ArangoDB' +author = 'Anthony Mahanna' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx_rtd_theme", + "sphinx.ext.autodoc", + "sphinx.ext.viewcode", +] +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'sphinx_rtd_theme' +html_static_path = ['_static'] +autodoc_member_order = "bysource" \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..05fbc410 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,80 @@ + +.. image:: https://github.com/ArangoDB-Community/ArangoRDF/actions/workflows/build.yml/badge.svg?branch=main + :target: https://github.com/ArangoDB-Community/ArangoRDF/actions/workflows/build.yml + +.. image:: https://github.com/ArangoDB-Community/ArangoRDF/actions/workflows/analyze.yml/badge.svg?branch=main + :target: https://github.com/ArangoDB-Community/ArangoRDF/actions/workflows/analyze.yml + +.. image:: https://coveralls.io/repos/github/ArangoDB-Community/ArangoRDF/badge.svg?branch=main + :target: https://coveralls.io/github/ArangoDB-Community/ArangoRDF?branch=main + +.. image:: https://img.shields.io/github/last-commit/ArangoDB-Community/ArangoRDF + :target: https://github.com/ArangoDB-Community/ArangoRDF/commits/main + +\ + +.. image:: https://img.shields.io/pypi/v/arango-rdf?color=3775A9&style=for-the-badge&logo=pypi&logoColor=FFD43B + :target: https://pypi.org/project/arango-rdf/ + +.. image:: https://img.shields.io/pypi/pyversions/arango-rdf?color=3776AB&style=for-the-badge&logo=python&logoColor=FFD43B + :target: https://pypi.org/project/arango-rdf/ + +\ + +.. image:: https://img.shields.io/github/license/ArangoDB-Community/ArangoRDF?color=9E2165&style=for-the-badge + :target: https://github.com/ArangoDB-Community/ArangoRDF/blob/main/LICENSE +.. image:: https://img.shields.io/static/v1?style=for-the-badge&label=code%20style&message=black&color=black + :target: https://github.com/psf/black +.. image:: https://img.shields.io/pepy/dt/arango-rdf?style=for-the-badge&color=282661 + :target: https://pepy.tech/project/arango-rdf + +\ + +.. image:: _static/adb_logo.png + :width: 50px + :alt: ArangoDB + +.. image:: _static/rdf_logo.png + :width: 50px + :alt: RDF + +\ + +ArangoRDF +--------- + +Convert RDF Graphs to ArangoDB, and vice-versa. + + +Requirements +============ + +- Python 3.8+ +- ArangoDB 3.10+ + +Installation +============ + +Latest Release + +.. code-block:: + + pip install arango-rdf + +Current State + +.. code-block:: + + pip install git+https://github.com/ArangoDB-Community/ArangoRDF + + +Contents +======== + +.. toctree:: + :maxdepth: 1 + + quickstart + rdf_to_arangodb_rpt + rdf_to_arangodb_pgt + specs diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..32bb2452 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/quickstart.rst b/docs/quickstart.rst new file mode 100644 index 00000000..2925a961 --- /dev/null +++ b/docs/quickstart.rst @@ -0,0 +1,74 @@ +Quickstart +---------- + +.. raw:: html + + Open In Colab + +.. code-block:: python + + from rdflib import Graph + from arango import ArangoClient + from arango_rdf import ArangoRDF + + db = ArangoClient().db() + + adbrdf = ArangoRDF(db) + + def beatles(): + g = Graph() + g.parse("https://raw.githubusercontent.com/ArangoDB-Community/ArangoRDF/main/tests/data/rdf/beatles.ttl", format="ttl") + return g + +**ArangoDB to RDF** + +**Note**: RDF-to-ArangoDB functionality has been implemented using concepts described in the paper +`Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches +`_. So we offer two transformation approaches: + +1. `RDF-Topology Preserving Transformation (RPT) <./rdf_to_arangodb_rpt.html>`_ +2. `Property Graph Transformation (PGT) <./rdf_to_arangodb_pgt.html>`_ + +.. code-block:: python + + # 1. RDF-Topology Preserving Transformation (RPT) + adbrdf.rdf_to_arangodb_by_rpt(name="BeatlesRPT", rdf_graph=beatles(), overwrite_graph=True) + + # 2. Property Graph Transformation (PGT) + adbrdf.rdf_to_arangodb_by_pgt(name="BeatlesPGT", rdf_graph=beatles(), overwrite_graph=True) + +**RDF to ArangoDB** + +.. code-block:: python + + # pip install arango-datasets + from arango_datasets import Datasets + + name = "OPEN_INTELLIGENCE_ANGOLA" + Datasets(db).load(name) + + # 1. Graph to RDF + rdf_graph = adbrdf.arangodb_graph_to_rdf(name, rdf_graph=Graph()) + + # 2. Collections to RDF + rdf_graph_2 = adbrdf.arangodb_collections_to_rdf( + name, + rdf_graph=Graph(), + v_cols={"Event", "Actor", "Source"}, + e_cols={"eventActor", "hasSource"}, + ) + + # 3. Metagraph to RDF + rdf_graph_3 = adbrdf.arangodb_to_rdf( + name=name, + rdf_graph=Graph(), + metagraph={ + "vertexCollections": { + "Event": {"date", "description", "fatalities"}, + "Actor": {"name"} + }, + "edgeCollections": { + "eventActor": {} + }, + }, + ) \ No newline at end of file diff --git a/docs/rdf_to_arangodb_pgt.rst b/docs/rdf_to_arangodb_pgt.rst new file mode 100644 index 00000000..5b9402bc --- /dev/null +++ b/docs/rdf_to_arangodb_pgt.rst @@ -0,0 +1,235 @@ +RDF to ArangoDB (PGT) +--------------------- +`Same graph, but lighter` + +What is PGT? +============ + +**PGT ensures that datatype property statements (i.e statements whose objects are Literals) +are mapped to node properties in the Property Graph (PG)**. + +Consider the following RDF Graph: + +.. code-block:: turtle + + @prefix ex: . + @prefix xsd: . + + ex:book ex:publish_date "1963-03-22"^^xsd:date . + ex:book ex:pages "100"^^xsd:integer . + ex:book ex:cover 20 . + ex:book ex:index 55 . + +PGT creates a PG consisting of a single node respresenting the RDF resource ``(ex:book)`` with +multiple properties representing property-object pairs from the RDF statements, such as ``(ex:index,55)``. +Distinguishing between **datatype** and **object property** statements, this approach transforms object +property statements to edges and datatype property statements to properties of the node representing the subject. +Unlike RPT, the resulting PG nodes represent only RDF resources or blank nodes while Literal Objects will +become properties: + +.. image:: _static/pgt.png + :width: 300px + :alt: PGT + + +The Algorithm belows formalizes the PGT approach. For each triple, check the type of the statement's object +(line 5), and based on that, decide to either create a node (if it does not yet exist, line 6) or +a property (line 13). + + +.. image:: _static/pgt_algorithm.png + :width: 500px + :alt: PGT Algorithm + + +Now, consider the following RDF-star Graph: + +.. code-block:: + + @prefix ex: . + + << ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 . + << ex:Mary ex:age 28 >> ex:certainty 1 . + +PGT transforms the embedded triple depending on its object; if it is an RDF resource, PGT converts it to an +edge. Otherwise, it converts the embedded triple into a node with a property +(lines 6-11) and fails to transform the asserted triple. The transformation of the triples from using PGT +results in a PG with a single node that makes it impossible to represent the ``(ex:certainty 1)`` triple, +since PGs do not support properties over other properties: + +.. image:: _static/pgt_star.png + :width: 400px + :alt: PGT Star Graph + +**Please Note:** The ``rdflib`` python package hasn't yet introduced support for +`Quoted Triples `_, so +ArangoRDF's support for RDF-star is based on `Triple Reification `_. + +As a result, the RDF-star Graph above can be processed with ArangoRDF as follows: + +.. code-block:: python + + from rdflib import Graph + from arango import ArangoClient + from arango_rdf import ArangoRDF + + data = """ + @prefix ex: . + @prefix rdf: . + + # << ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 . + # << ex:Mary ex:age 28 >> ex:certainty 1 . + + [] a rdf:Statement; + rdf:subject ex:Mary; + rdf:predicate ex:likes; + rdf:object ex:Matt ; + ex:certainty 0.5 . + + [] a rdf:Statement; + rdf:subject ex:Mary; + rdf:predicate ex:age; + rdf:object 28 ; + ex:certainty 1 . + """ + + rdf_graph = Graph() + rdf_graph.parse(data=data, format="turtle") + + db = ArangoClient().db() + adbrdf = ArangoRDF(db) + adbrdf.rdf_to_arangodb_by_pgt(name="DataPGT", rdf_graph=rdf_graph, overwrite_graph=True) + + +ArangoDB Collection Mapping Process +==================================== + +The **ArangoDB Collection Mapping Process** is defined as the algorithm used to map +RDF Resources to ArangoDB Collections. In PGT, the ArangoDB Collections generated are +are based on the ``rdf:type`` nature of the RDF Resource / Statement. + +Let's consider the following RDF Graph: + +.. code-block:: turtle + + @prefix ex: . + @prefix adb: . + @prefix rdfs: . + @prefix rdf: . + + ex:B rdfs:subClassOf ex:A . + ex:C rdfs:subClassOf ex:A . + ex:D rdfs:subClassOf ex:C . + + ex:alex rdf:type ex:A . + + ex:sam ex:age 25 . + ex:age rdfs:domain ex:A . + + ex:john rdf:type ex:B . + ex:john rdf:type ex:D . + + ex:mike rdf:type ex:G . + ex:mike rdf:type ex:F . + ex:mike rdf:type ex:E . + + ex:frank adb:collection "Z" . + ex:frank rdf:type ex:D . + + ex:bob ex:name "Bob" . + + ex:alex ex:knows ex:bob . + +The PGT ArangoDB Collection Mapping Process would produce the following ArangoDB Collections: + +1. A (Vertex Collection) +- ``ex:alex`` +- ``ex:sam``: Although this RDF Resource has no ``rdf:type`` associated statement, we can infer from the domain of the property it uses (``ex:age``) that it is of type ``ex:A``. + +2. D (Vertex Collection) +- ``ex:john``: This RDF Resource has 2 ``rdf:type`` statements, but ``ex:D`` is more verbose than ``ex:B`` according to the ``rdfs:subClassOf`` Taxonomy. + +2. E (Vertex Collection) +- ``ex:mike``: This RDF Resource has multiple ``rdf:type`` statements, with +none belonging to the ``rdfs:subClassOf`` Taxonomy. +Therefore, the objects are sorted alphabetically, and ``ex:E`` is the first +in the list. + +3. Z (Vertex Collection) +- ``ex:frank``: This RDF Resource has an ``adb:collection`` statement associated +to it, which is prioritized over any other ``rdf:type`` +statement it may have. Think of it as an override mechanism to any +other ``rdf:type`` statement. + +4. UnknownResource (Vertex Collection) +- ``ex:bob``: This RDF Resource has neither an ``rdf:type`` statement, +nor an ``adb:collection`` statement associated to it. It +is therefore placed under the ``UnknownResource`` Collection. + +5. knows (Edge Collection) +- ``ex:alex ex:knows ex:bob``: This RDF Resource is an edge, and therefore is placed under the +``knows`` Edge Collection. The local name of the predicate is **always** used +as the name of the Edge Collection. + +Thus, we can formalize the ArangoDB Collection Mapping Process as follows: + + +1. Any RDF Statement of the form ``(subject "X")`` +will map the Subject to the ArangoDB Vertex Collection ``X``. + +2. If 1. is not satisfied, then any RDF Statement of the form +``(subject object)`` +are considered in the following way: + +2.1. If an RDF Resource only has one `rdf:type` statement, +then the local name of the RDF Object is used as the ArangoDB +Document Collection name. For example, +``( )`` +would create an JSON Document for ````, +and place it under the "Person" Document Collection. +**NOTE**: The RDF Object will also have its own JSON Document +created, and will be placed under the ``"Class"`` +Document Collection. + +2.2 If an RDF Resource has multiple ``rdf:type`` statements, +with some (or all) of the RDF Objects of those statements +belonging in an ``rdfs:subClassOf`` Taxonomy, then the +local name of the "most specific" Class within the Taxonomy is +used (i.e the Class with the biggest depth). If there is a +tie between 2+ Classes, then the URIs are alphabetically +sorted & the first one is picked. + +2.3 If an RDF Resource has multiple ``rdf:type`` statements, with +none of the RDF Objects of those statements belonging in an +``rdfs:subClassOf`` Taxonomy, then the URIs are +alphabetically sorted & the first one is picked. The local +name of the selected URI will be designated as the Document +collection for that Resource. + + +**Final Remarks** + +We are welcome to suggestions on how to improve the ArangoDB Collection Mapping Process for PGT, +as we understand that it is not perfect. + +Consider overriding the `ArangoRDFController <./specs.html#arango_rdf.controller.ArangoRDFController>`_ Class +to implement your own ArangoDB Collection Mapping Process for PGT. + +Supported Cases +=============== + +**Note**: RDF-to-ArangoDB functionality has been implemented using concepts described in the paper +`Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches +`_. + +The paper presents a systematic list of test cases that transformation approaches need to fulfill. These +test cases range from simple RDF Graphs to complex RDF-star Graphs. + +ArangoRDF's PGT interface can be observed `here <./specs.html#arango_rdf.main.ArangoRDF.rdf_to_arangodb_by_pgt>`_. + +`View how ArangoRDF's PGT transformation approach performs on these test cases in +Colab `_. + +.. image:: _static/cases.png + :width: 500px + :alt: Cases diff --git a/docs/rdf_to_arangodb_rpt.rst b/docs/rdf_to_arangodb_rpt.rst new file mode 100644 index 00000000..1659b49d --- /dev/null +++ b/docs/rdf_to_arangodb_rpt.rst @@ -0,0 +1,152 @@ +RDF to ArangoDB (RPT) +--------------------- +`Virtualizng ArangoDB as a Triple Store` + +What is RPT? +============ + +**The RDF-topology Preserving Transformation (RPT) algorithm preserves the +RDF graph structure by transforming each RDF statement into an edge in the Property Graph (PG).** + +Consider the following RDF Graph: + +.. code-block:: turtle + + @prefix ex: . + @prefix xsd: . + + ex:book ex:publish_date "1963-03-22"^^xsd:date . + ex:book ex:pages "100"^^xsd:integer . + ex:book ex:cover 20 . + ex:book ex:index 55 . + +RPT converts the triple ``(ex:book, ex:index, 55)`` into two +nodes ``(ex:book)`` and ``(55)``, connected by an edge ``(ex:index)``. All other triples +involving RDF resources, blank nodes, or literal values can be transformed in +a similar way so that we obtain the Property Graph below: + +.. image:: _static/rpt.png + :width: 300px + :alt: RPT Graph + + +The Algorithm below formalizes the RPT approach. For each triple, create a node for the subject (line +3) and the object (line 5), with an edge connecting them (line 12) - of course avoiding duplicate +nodes for the same IRIs. + + +.. image:: _static/rpt_algorithm.png + :width: 500px + :alt: RPT Algorithm + + +Now, consider the following RDF-star Graph: + +.. code-block:: + + @prefix ex: . + + << ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 . + << ex:Mary ex:age 28 >> ex:certainty 1 . + +ArangoRDF's RPT transformation for RDF-star Graphs is slightly different from the +transformation proposed in the paper. In order to preserve the concept of **virtualizing ArangoDB +as a Triple Store**, conversion for RDF-star statements is identical to RDF triples. This is not +the case for the RPT transformation proposed in the paper, which proposes to add the `ex:certainty 1` as +an edge attribute to the edge connecting the nodes `ex:Mary` and `28`. Instead, ArangoRDF's RPT +transformation expresses ``(..., ex:certainty, 1)`` as its own edge: + +.. image:: _static/rpt_star.png + :width: 400px + :alt: RPT Star Graph + +**Please Note:** The ``rdflib`` python package hasn't yet introduced support for +`Quoted Triples `_, so +ArangoRDF's support for RDF-star is based on `Triple Reification `_. + +As a result, the RDF-star Graph above can be processed with ArangoRDF as follows: + +.. code-block:: python + + from rdflib import Graph + from arango import ArangoClient + from arango_rdf import ArangoRDF + + data = """ + @prefix ex: . + @prefix rdf: . + + # << ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 . + # << ex:Mary ex:age 28 >> ex:certainty 1 . + + [] a rdf:Statement; + rdf:subject ex:Mary; + rdf:predicate ex:likes; + rdf:object ex:Matt ; + ex:certainty 0.5 . + + [] a rdf:Statement; + rdf:subject ex:Mary; + rdf:predicate ex:age; + rdf:object 28 ; + ex:certainty 1 . + """ + + rdf_graph = Graph() + rdf_graph.parse(data=data, format="turtle") + + db = ArangoClient().db() + adbrdf = ArangoRDF(db) + adbrdf.rdf_to_arangodb_by_rpt(name="DataRPT", rdf_graph=rdf_graph, overwrite_graph=True) + + +ArangoDB Collection Mapping Process +=================================== + +The **ArangoDB Collection Mapping Process** is defined as the algorithm used to map +RDF Resources to ArangoDB Collections. In RPT, the ArangoDB Collections generated are +consistent: + +1. ``{Name}_URIRef``: The Vertex collection for ``rdflib.term.URIRef`` resources. +2. ``{Name}_BNode``: The Vertex collection for ``rdflib.term.BNode`` resources. +3. ``{Name}_Literal``: The Vertex collection for ``rdflib.term.Literal`` resources. +4. ``{Name}_Statement``: The Edge collection for all triples/quads. + +Using the python example from above, the RDF Resources of your RDF Graph would be stored under the following ArangoDB Collections: + +1. DataRPT_URIRef + - ``ex:Mary`` + - ``ex:Matt`` +2. DataRPT_BNode + - ``[]`` (1) + - ``[]`` (2) +3. DataRPT_Literal + - ``0.5`` + - ``1`` +4. DataRPT_Statement + - ``ex:Mary -> ex:likes -> ex:Matt`` + - ``ex:Mary -> ex:age -> 28`` + - ``(ex:Mary -> ex:likes -> ex:Matt) -> ex:certainty -> 0.5`` + - ``(ex:Mary -> ex:age -> 28) -> ex:certainty -> 1`` + +This is the consistent naming scheme for all ArangoRDF RPT transformations. The name of the +RDF Graph is used as a prefix for the 3 Vertex Collections and the 1 Edge Collection. + +Supported Cases +=============== + +**Note**: RDF-to-ArangoDB functionality has been implemented using concepts described in the paper +`Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches +`_. + +The paper presents a systematic list of test cases that transformation approaches need to fulfill. These +test cases range from simple RDF Graphs to complex RDF-star Graphs. + +ArangoRDF's RPT interface can be observed `here <./specs.html#arango_rdf.main.ArangoRDF.rdf_to_arangodb_by_rpt>`_. + +`View how ArangoRDF's RPT transformation approach performs on these test cases in +Colab `_. + +.. image:: _static/cases.png + :width: 500px + :alt: Cases diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..3255fa30 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,5 @@ +sphinx_rtd_theme +python-arango +cityhash +rdflib +rich \ No newline at end of file diff --git a/docs/specs.rst b/docs/specs.rst new file mode 100644 index 00000000..78e45ece --- /dev/null +++ b/docs/specs.rst @@ -0,0 +1,18 @@ +API Specification +----------------- + +This page contains the specification for all classes and methods available in +ArangoRDF. + +ArangoRDF +========= + +.. autoclass:: arango_rdf.main.ArangoRDF + :members: + + +ArangoRDFController +=================== + +.. autoclass:: arango_rdf.controller.ArangoRDFController + :members: diff --git a/examples/ArangoRDF.ipynb b/examples/ArangoRDF.ipynb index ab253c9a..141c0c03 100644 --- a/examples/ArangoRDF.ipynb +++ b/examples/ArangoRDF.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "VBMhu_S_A2-3" @@ -9,12 +8,11 @@ "source": [ "# **ArangoRDF**\n", "\n", - "\"rdf\"\n", - "\"rdf\"" + "\"rdf\"\n", + "\"rdf\"" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "44mc2EvIAzDy" @@ -32,9 +30,9 @@ "outputs": [], "source": [ "%%capture\n", + "!pip install arango-datasets\n", "!pip install adb-cloud-connector\n", - "!pip install arango-rdf==0.1.0\n", - "!git clone https://github.com/ArangoDB-Community/ArangoRDF.git " + "!pip install arango-rdf" ] }, { @@ -45,18 +43,34 @@ }, "outputs": [], "source": [ - "from adb_cloud_connector import get_temp_credentials\n", - "from arango import ArangoClient\n", "import json\n", "\n", - "from rdflib import Graph, ConjunctiveGraph, URIRef, Literal, Namespace\n", - "from rdflib.namespace import RDFS, XSD\n", + "# ArangoDB\n", + "from arango import ArangoClient\n", + "from arango_rdf import ArangoRDF\n", + "from arango_datasets import Datasets\n", + "from adb_cloud_connector import get_temp_credentials\n", "\n", - "from arango_rdf import ArangoRDF" + "# RDF\n", + "from rdflib import Graph, ConjunctiveGraph, URIRef, Literal, Namespace\n", + "from rdflib.namespace import RDFS, XSD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9L0Q4cGTMGMG" + }, + "outputs": [], + "source": [ + "def get_graph(data: str, is_conjunctive_graph: bool = False) -> Graph:\n", + " g = ConjunctiveGraph() if is_conjunctive_graph else Graph()\n", + " g.parse(data=data, format='trig' if is_conjunctive_graph else 'ttl')\n", + " return g" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "yRuJ3OIGE2Yr" @@ -66,7 +80,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "z1SFNei_imUf" @@ -86,7 +99,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "4VzvpJ2EuuMJ" @@ -96,13 +108,12 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "ssq_ohNZu0q_" }, "source": [ - "[RDFLib](https://github.com/RDFLib/rdflib) is a pure Python package for working with RDF. \n", + "[RDFLib](https://github.com/RDFLib/rdflib) is a pure Python package for working with RDF.\n", "\n", "RDFLib aims to be a pythonic RDF API. RDFLib's main data object is a `Graph` which is a Python collection of RDF Subject, Predicate, Object Triples:\n", "\n", @@ -113,7 +124,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xiRxsYqY52BJ" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xiRxsYqY52BJ", + "outputId": "adcd539c-dcf5-4e89-ab50-091406af9832" }, "outputs": [], "source": [ @@ -125,7 +140,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "tibPlFLZ56cZ" @@ -148,7 +162,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "f1tb4nb15_Gl" @@ -172,7 +185,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "1dz2_jcA6Cls" @@ -187,7 +199,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "G-zlPMs36LF3" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G-zlPMs36LF3", + "outputId": "db3d3e78-fd84-4f55-e56d-be28dd990191" }, "outputs": [], "source": [ @@ -199,7 +215,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "4UvegpOD6QBC" @@ -223,7 +238,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "5ag8fFkN6cDK" @@ -237,7 +251,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "VBiHxQ8I6ckC" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VBiHxQ8I6ckC", + "outputId": "4bad19d3-f29a-4740-c0d3-e1278f9156a6" }, "outputs": [], "source": [ @@ -245,7 +263,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "fU3ZW4Up6kft" @@ -269,13 +286,12 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "KnQifktFAxHx" }, "source": [ - "# Create a Temporary ArangoDB Cloud Instance" + "# Setup ArangoDB" ] }, { @@ -286,7 +302,7 @@ "base_uri": "https://localhost:8080/" }, "id": "ETS8l_NSAv0F", - "outputId": "b32451f1-b737-4fc9-a7ee-443265acef31" + "outputId": "e4242d73-6443-4292-9977-c3e9670d17ee" }, "outputs": [], "source": [ @@ -295,69 +311,15 @@ "print(json.dumps(con, indent=2))\n", "\n", "# Connect to the db via the python-arango driver\n", - "db = ArangoClient(hosts=con[\"url\"]).db(con[\"dbName\"], con[\"username\"], con[\"password\"], verify=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "7y81WHO8eG8_" - }, - "source": [ - "# Data Import" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "BM0iRYPDeG8_" - }, - "source": [ - "For demo purposes, we will be using the [ArangoDB Game Of Thrones Dataset](https://github.com/arangodb/example-datasets/tree/master/GameOfThrones)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7bgGJ3QkeG8_", - "outputId": "fbbe33ff-9df9-459a-93e1-3232ea3733bc" - }, - "outputs": [], - "source": [ - "!chmod -R 755 ArangoRDF/\n", - "!./ArangoRDF/tests/tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --replication-factor 3 --input-directory \"ArangoRDF/tests/data/adb/got_dump\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IkWQ9W4UZcIz" - }, - "outputs": [], - "source": [ - "if not db.has_graph(\"GameOfThrones\"):\n", - " db.create_graph(\n", - " \"GameOfThrones\",\n", - " edge_definitions=[\n", - " {\n", - " \"edge_collection\": \"ChildOf\",\n", - " \"from_vertex_collections\": [\"Characters\"],\n", - " \"to_vertex_collections\": [\"Characters\"],\n", - " },\n", - " ],\n", - " orphan_collections=[\"Traits\", \"Locations\"],\n", - " )" + "db = ArangoClient(hosts=con[\"url\"]).db(\n", + " con[\"dbName\"],\n", + " con[\"username\"],\n", + " con[\"password\"],\n", + " verify=True\n", + " )" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "QfE_tKxneG9A" @@ -367,13 +329,12 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "kGfhzPT9eG9A" }, "source": [ - "Connect ArangoRDF to our temporary ArangoDB cluster:" + "Connect ArangoRDF to our temporary ArangoDB instance:" ] }, { @@ -384,7 +345,7 @@ "base_uri": "https://localhost:8080/" }, "id": "oG496kBeeG9A", - "outputId": "57cb6237-dfea-48b6-a2c3-af4ad52cff7b" + "outputId": "ca895acc-a9ad-4287-8364-7591618b8b5b" }, "outputs": [], "source": [ @@ -392,7 +353,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "znQCjOwt7zBz" @@ -402,36 +362,37 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "0qry3Bcy-160" }, "source": [ - "#### RPT vs PGT" + "#### ❗ RPT vs PGT ❗\n", + "\n" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "0ONWNS6t8x7A" }, "source": [ - "RDF-to-ArangoDB functionality has been implemented using concepts described in the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781).\n", + "ArangoRDF's RDF-to-ArangoDB functionality has been implemented using concepts described in the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781).\n", "\n", - "In other words, ArangoRDF offers 2 RDF-to-ArangoDB transformation methods:\n", + "**TL;DR**:\n", "\n", - "1. RDF-topology Preserving Transformation (RPT): `ArangoRDF.rdf_to_arangodb_by_rpt()`\n", - "2. Property Graph Transformation (PGT): `ArangoRDF.rdf_to_arangodb_by_pgt()`\n", + "ArangoRDF has 2 RDF-to-ArangoDB transformation methods:\n", + "\n", + "1. **RDF-topology Preserving Transformation** (RPT): `ArangoRDF.rdf_to_arangodb_by_rpt()`\n", + "\n", + "2. **Property Graph Transformation** (PGT): `ArangoRDF.rdf_to_arangodb_by_pgt()`\n", "\n", "RPT preserves the RDF Graph structure by transforming each RDF Statement into an ArangoDB Edge.\n", "\n", - "PGT on the other hand ensures that Datatype Property Statements are mapped as ArangoDB Document Properties." + "PGT ensures that Datatype Property Statements (i.e Literal Statements) are mapped as ArangoDB Document Properties." ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "U_sBs3jc96e3" @@ -452,20 +413,59 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "mRutdKii-Pk5" }, "source": [ - "#### Simple RPT & PGT Examples" + "#### Cases" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2PWrF6C5VByv" + }, + "source": [ + "RDF Graph Cases taken from the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "cy_BWXK2AX5n" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "fc377f11c10c4d968c8a6caac22e0012", + "06aca56f72fb4329b49cfcc532ac8116", + "358dc9cbaa2f47f6aa3b14963b022222", + "fb6fc4469af9424c9c8eaaec53ddf478", + "2ddfd1e64d4747f8b3cc576d462d5f56", + "c1a0f2be84cd45749b0043f6510a4bfa", + "eee2941003754994b27156607ed4d79a", + "1d6d26d7b5a642529791ac860d335e29", + "999885ce87084d309d1c8c0972124094", + "d867c72e97bd48b686aac05d789729fe", + "e28d9f03ff10486fbe6f47ea396bc610", + "2cd5ecb36d81498595f558f842e23c05", + "9b60d80acd1140eb91da4ee24e3aec65", + "edc4bc9e70c3451299599dc199951288", + "407cfb7d093540daa3e77ed151028666", + "e849e373b5fd4798b5efae255bba65e7", + "ecf4cf0278e4442188384c7e7c2d7e56", + "4353e4aefa3b4d9a9d57249b27df4932", + "6df2082e6f484dc79472fccacc27c996", + "0d86bf821be447f4ab655cd136af247d", + "1ba7a0ca8b0646b8a5d9cb4ec400e03b", + "024dbdf4ef6d411f8f8a6e25603c68a7", + "3675e6b10c0748e387629f471847295a", + "ac58fe66dce145f0be7f770a26039acf" + ] + }, + "id": "cy_BWXK2AX5n", + "outputId": "2ace5ff0-1603-4719-fe13-dcace5f832bf" }, "outputs": [], "source": [ @@ -479,18 +479,46 @@ "ex:alice ex:meets ex:bob .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "9BFNRAzLDmzU" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 150, + "referenced_widgets": [ + "34082cf6eb994d0e8dc1c5a32103e6a8", + "a601dcef909d423d89e184f7afefd15c", + "39bbccb261f74e9696665119136f35ee", + "89d11dbcd6e34779a029e0cca10286a5", + "03740e7e6199495cbaebf039d6fea20a", + "e9d399758a9c466188e5ed4c7076832a", + "7f35f5dbf07e47f78d51f42f62c42258", + "013737bac24c4fc19871485909ee3d44", + "82621e9cb2ba47baafa2c1580d91740b", + "99fbd40f55514a0087206be19f5b96db", + "a2b0b7c7cc6a4057b8743464c0fb33a8", + "7c95cb7b6bbf4f018a598a9ec2e7d8c9", + "d66eb2a7b7f943e2b9bc8f3c3567da79", + "d911bd1cb75941509e9f3578e037785b", + "b5aaff1d7205458b8a9ef5457d2e3600", + "f2c7dcc0c0c848e18722c628f78b8676", + "54182c5e13f84178b64673a1ddfc3a42", + "52c95751c36a41249e00de51f9280ed5", + "9cd12ef1fe0c467cb7f31ce82200f9e6", + "9e955e0fd94e4b388da05e1056c13e1e", + "367eabfb0fd24998835426983d7565a1", + "89e21dfb027c495f9a9524ec26ff204e", + "e8be2a32f6d341998985b2c1f4e3d9c9", + "95d6f533e8a240c3b0d4aad2e9580335" + ] + }, + "id": "9BFNRAzLDmzU", + "outputId": "5038bf6e-b8be-410f-afe4-3dcdca407898" }, "outputs": [], "source": [ @@ -509,18 +537,46 @@ "ex:Lee a ex:Person .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "E6t4VRcsD2m7" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "36998aa1db0d4404a02bef5abfa995c8", + "f529a9ce3a0a4fdf832eae87d164d458", + "52cbbdbb898f48deae681ff01d1468c8", + "4f58d9bac09c404c9f2b1f7d6007109d", + "b54aeadd1028418a9c44d660dd9a44f9", + "d61fc357d96646cbacbf74a072e3b738", + "8f588230f1de4c58a2e8dbc1b466f0e6", + "6c5dc20ab389471e993a2407b20e01cc", + "5c8ea494175c47e1b55304e2730df295", + "9d506f434f5d4df7be610f8b40fcd390", + "1f9945bbb3924f15999d790fed89863f", + "03a99f41fc0a4353bfc5efb093e552ac", + "de8a98213fa542af9f90496371ce7058", + "bb8162c2ad224fbc87044aa6b7dca2a9", + "41c5ae5ef1f1453ba249725f961f6b47", + "f433163973a148f7ae20eaa83425e8b9", + "663d9df312bf4c44994eca45cc5dcf93", + "7f8a2efde7f242f4af578c23d5d72004", + "442ab928de054d89834465fa7d3f6c90", + "7aac7eecd5364b3d976a3a7c108cde46", + "5474d353569b4aa99ca7b0cfba1a3a45", + "7a61723dadad4e29858db789b0befedd", + "324aaf266fbe4a73a48096790b2b3a8a", + "bcaf27bfd2b94b088aee76f8c1c0b394" + ] + }, + "id": "E6t4VRcsD2m7", + "outputId": "40d573e1-c1fc-449c-88c7-4ad8e1c44f29" }, "outputs": [], "source": [ @@ -534,18 +590,46 @@ "ex:mentorJoe ex:alias ex:teacher .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "NEDGhDfzEEhg" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "d09c5ec60dce44c1a554ab5708e1a06a", + "4e9d7516d4b447e9af814d0ef8e1a33e", + "299cedcd596747f7a8837d2ef1a25f5c", + "2f3cc4e6401444d1a3154659afc9efab", + "5498eaee5d4147e7837cb6c6c563dfbc", + "a8d4da8fc9f9471e9bc5d328b4ca8d90", + "62199c1525d34669b32ec33744030077", + "554c15b9173641b199741a03138c3ec5", + "17719aef6c534bddabefe7a8842e743b", + "fbc6745df8724e9e9379e3ab3b07b2c5", + "ccbc35240b3648ae9b30318be78f7b79", + "f3bb03a8be4645cfa5f39fecdee44057", + "b81d313da9914c0dbc9401bb61e36cd2", + "306ec886b3264c6fa6df970754f7f603", + "e9c373c0a0b14537bcca1d43a4aa7d58", + "6bd89d16c4d649538dc7213f8abc7aed", + "06838a4ca6e3487abf98d48ac909f144", + "746745f6f03543e08fd4429cc5cc900f", + "1656f5d65cf74bc09f36c42d6805e634", + "db682b0171354a50bc8e7ae48d5d49f1", + "e108a1590816436995542b612fedce0c", + "f777d49fe0d0408f8df7051c16a726e5", + "094c0ec357ef44c395c39ae90dd66e7a", + "f6ddbd6c083449d1b675b1829c5b682f" + ] + }, + "id": "NEDGhDfzEEhg", + "outputId": "e3e7726e-f2fa-44c0-9f4a-0923128887a3" }, "outputs": [], "source": [ @@ -563,18 +647,46 @@ "ex:supervise rdfs:subPropertyOf ex:administer .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "WraNcreKcJ35" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "4cb8bd60a6174fdaa24c09f9a852fa18", + "14139b121edc4b42bf05b1bf35f232ef", + "59c0abf74b4d4276ba5bc305f28d26a8", + "5382e4076e9d4b058082df8c722e0668", + "5eb7089127fb481a93e44a1468c01914", + "260648c894104cb092ede5d76ef44111", + "e97722a842234f0da935308222ef07af", + "037bf3dcddc04024ab27ab2c67c6e494", + "ee08f8a0423141508d6163d09448e7a4", + "3f2f2a4d7c484682bf12ded64a1ceee7", + "19f32708e8e148a4a38ea7d27b931a2e", + "b213dfe9dbe6485ba9ce050603aa69f4", + "56a5349593d642ebaf100d6a50abf506", + "bb3de533a75145bba916c21d660249ce", + "b7c35383db9149c0bd7bc1b6c845e7c7", + "908de16ce10a496b991f81cfa695f0e4", + "31cdeffecb6e4de199d9b8925ec7a567", + "86cabf0aa5da4a2bb523713b7f70e2c8", + "c3e8856f18d64146a238e5ec533ba596", + "81b1364ffbd34ef688fb059f9e80afd1", + "518456cbb9b743f38914894954dd8777", + "bea5ba1e9db740a29147d2a2f105c8a9", + "a68461a127d2438fabf18b90f0e7a0f3", + "b9a655403dd74da18f452ca364b843d7" + ] + }, + "id": "WraNcreKcJ35", + "outputId": "0007a8f1-d9bf-4895-daf6-7366bef50ae4" }, "outputs": [], "source": [ @@ -592,15 +704,46 @@ "g = Graph()\n", "g.parse(data=data)\n", "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "00ooim92Ekxv" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "ed9195442d784e6d8550886ee8d67aa5", + "8e09ff0eb87345a88389b0a6780c1167", + "40ba8687516e4923a56bd8354dc97838", + "ccaa03e4315e4945b5fd1c01e084369d", + "117b18d0bbc142ccb0fe1f5598a3cd1b", + "9f28ee10e68c4eb4b4b82076d5324212", + "e9716ce6050143ad814e61e4be7c29c4", + "fa8313cdc1884277a8b7b627d7873f75", + "a5878d1b00ea420a80da1504a988cebd", + "90cb248476934ee496a5c8e1ef2b5c31", + "160f5882af964f5887dc015b767426c4", + "37fc823816794e83a098931026bde2ec", + "b759d7fc0b0143e2bb2b1ca418deca36", + "1efe1b9781934efc9bf72d2632310f77", + "ca414902580045019738e070dd4d345b", + "defc0ab1aa6f4534b882322c9809cabf", + "05a0ce7410fc476085ef3a25bf49cc79", + "d02585418dad49a89330b587b87e9c5a", + "45c23924820f4dc28941d54522152446", + "ec967562785641e6b0c0aaedd4946827", + "b4187694d84a486c96214682fa0b06e8", + "b2212838b5dc498cb7f633a7ceae77f6", + "e5b22f764cb549bea4bafbbab1c80313", + "f4c3e03b26e84299a72432247e3211c2" + ] + }, + "id": "00ooim92Ekxv", + "outputId": "ee60ee36-1fa1-4118-c3c8-842e79f1c364" }, "outputs": [], "source": [ @@ -617,18 +760,46 @@ "ex:book ex:index \"55\" .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "UmIn_SZWccN2" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "573e8e1ec5794c519349919b0c644e0c", + "841e9c1810c640d1bec6c739f60f11a8", + "d2188b7ea4ec494591acd144708bd77a", + "f43396b324cf46b4b10c0f21e3917631", + "8526056c9dfe43f9942f53afdd79152d", + "5d47d305fe12407cbc727cfa15ea56b2", + "a3a87aebeb7e47e393ed6e73309d0368", + "fc64965fc2bb4303a2605992562de2df", + "edba16230f7c4f9db1cbaa9ad78b8bbc", + "562c5eb6af7b48cab01a04d36df6d8a0", + "73467dd00a6a45398be0e336a67e3379", + "a27f4bc191244980a4569f1380509b24", + "38e4ec950dfb48c1b86dfa51b59bd9f6", + "52737430028e42b493963e2ef404902d", + "e8a65707044c47cea44e778d77ecd8a5", + "ab8cd606859e4bfe8bb71c6096f8aa33", + "b8b1c716475e4043af7919e06253f606", + "8008d0fc5ee848c5a60ae9fc6e0af2ca", + "9c73cea9af174a978ed87abd757dd466", + "7ed46570893d4123a718f77890e84081", + "075b504f72064efaaaa1ee09afdc6a9f", + "0700821fd3764f7da3e204d15601d063", + "a96caf05dfcf41cb848229c08969a36b", + "906fe2e4e22940bc9fd816cefc70277a" + ] + }, + "id": "UmIn_SZWccN2", + "outputId": "a320ec95-f1af-450b-a706-0d49abb9e12f" }, "outputs": [], "source": [ @@ -643,18 +814,46 @@ "ex:book ex:title \"Bog\"@da.\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "SQaaqperccbA" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "18a76eb8f00b4c9d93eb862457c93604", + "be38c1c4704e42eaab72fa917a31551f", + "5819a9a679784e53979fe59ac6fdc6c8", + "ea91f40791f247118cbfd30f0a8e0209", + "1368dab9ca5d48baabedaa27e63a6608", + "cf1f8b24c7b64859b87bfea6a16f7dd6", + "1118d6ebf1f74180b2a3fb95d17d5f57", + "34f212a9207b4395a7ab6cee9990ba5a", + "b4b3ea80874b4f6da320f1c11cf08363", + "5a74235176fb4af3831eb25cd2e97262", + "99edc92ed36a4566890b4da4ed68c123", + "0f786717b0094fe785ea05fec93bdf06", + "03b0d5a081d84381929c663657fa266e", + "694a30f6080d488cbc087bdb2d91e91e", + "aaf936790e47461386f187f15e2fb58c", + "c9f80d60eb294a449579d9673201b333", + "e9dcbbb06e584549ba0adbf846ad8999", + "8ae0c9e3aa7841308a73424fa9fac059", + "c050e033d5614e97a63b09513cb6eb10", + "9575b3b304784e31a335e371c2d5f3a5", + "a43f6762475244aabdff63426a9e1423", + "5772089896234e52aa40e3daadc38d30", + "1611c20c4e834c76ae76a4f119db71c8", + "3b81b38993c14de68df2ff40f0a4f590" + ] + }, + "id": "SQaaqperccbA", + "outputId": "f35afe70-71d0-498c-a08f-b2ba28b5e9a5" }, "outputs": [], "source": [ @@ -666,18 +865,46 @@ "ex:List1 ex:contents (\"one\" \"two\" \"three\").\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "r0kHJogZFEKO" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 150, + "referenced_widgets": [ + "eeaeab711cca4776bbf9d852b61d2744", + "f65b348bdebe446ab8381f928147bb67", + "c643b1c5505f4f0c83895c0b3e726657", + "900ea2b6683f4dbc89aa1360936bad45", + "8cf060d8fc7f4ecf80b318a11473c020", + "b04978aca95e428ab04c656b3d39fdc5", + "4f691259e5674b3da1f46f11075d5428", + "456a8dd834234dbabcc65cc4b98f2b2d", + "6a78b165f4aa4e04bb0a40d531d1facc", + "d544f1ae4c1c4355b02b6b58ffe4e227", + "7628cbddf01341e0ad612263dba3ed06", + "1167bf2328584950a2fe44c959d88a85", + "13a2bad0545b4813b411dd2059dcdaab", + "23537334ef5f4bf7871e86250eab22c9", + "b5e30a4917db413081b1003aac893141", + "8add49e7aff949c8b6aa86ecf1b8404c", + "750e03bd0e7340ea94112bb5971b556d", + "5871fa9c17ca4dbbad7c110842b1c43c", + "85eb298ec6d649c4bef732d7776e1936", + "be66a8a9c8544a8c9fa4d86468c57d05", + "341b818f59ba43d4b69166e655dd9765", + "4624a85e55f54bbf9488a44dcb872a06", + "283c95e105104ea6a5b235ede8a1a2ab", + "d4f26543a05649eab36d2d2e430bf868" + ] + }, + "id": "r0kHJogZFEKO", + "outputId": "790750bc-b180-4bf4-e847-ac279c59f5dc" }, "outputs": [], "source": [ @@ -690,18 +917,46 @@ "_:c ex:country \"Canada\" .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "noAXcHOJFJvG" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 150, + "referenced_widgets": [ + "1fc43d8d945449ff8bf650a7168f3175", + "c06094c52f354bf887b9573ef728ba6f", + "597dc766da6d4c65a6e526f29d8001ad", + "6c9fda1cf8f441649b19421140844eca", + "222ddec771e2487088f38f5d8abc93d6", + "ca9ee619db5145a7b14ef9eaf7b282ef", + "90c5c0ba3d0b4e8e8194dc0bc254b191", + "d773e247a00b40f592d2b8a355d67eae", + "5f82c603b82e424daed6d81cc12810b3", + "94d0232fdf1a46b8ad11059dbf8162dc", + "cb0587dfec79428a8171b6e29aa7b75c", + "a8a41f5965ec45ed90c876c0b309b044", + "fca5445e52624e6aabf3acea3b614d1a", + "d793b865039d47fa966eb68c790b62a6", + "afd95022a638474ebd254a4e365e0f5f", + "787d5500e7da4133a75f8df78b8466fa", + "b98c2692fc3c4837a405d58e2cb2aede", + "a05e21f6174647dca22307a6ae256d40", + "549cc0eeb21e4816af1ce48c6992db04", + "863bb5095d89492cb6153b3bbde5b104", + "ba683955f5904919ad09b805dbbf6a5c", + "ddf39a4764c84bafb454073773b0b5f3", + "48867504843843389281e9c6c12953c4", + "84276bda9c6c4c7280423540f4695001" + ] + }, + "id": "noAXcHOJFJvG", + "outputId": "554e4310-e28b-40a7-a701-a2ac91affdaf" }, "outputs": [], "source": [ @@ -720,6 +975,7 @@ " ex:Monica ex:name \"Monica\" .\n", " ex:Monica ex:homepage .\n", " ex:Monica ex:hasSkill ex:Management .\n", + " ex:Monica ex:dateOfBirth \"1963-03-22\".\n", "}\n", "\n", "ex:Graph2 {\n", @@ -731,18 +987,48 @@ "}\n", "\"\"\"\n", "\n", - "cg = ConjunctiveGraph()\n", - "cg.parse(data=data, format='trig')\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", cg, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", cg, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "gtHKG7PiGyyF" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134, + "referenced_widgets": [ + "7f4735b6532b44bf9e2a4deb1385e677", + "e86234b816a148f482c6f0d7fc241675", + "fbb89f7404f644b4adcb2e011bd39a2f", + "d2a5ec6f84244ed58b1cbf3fadcffe9d", + "f3091863c2ce495b8846ca16c240bac6", + "3c6e454224c346ae820de03d069583ad", + "1d6a2e9d98e6431db87f0f20de68a2db", + "ef946e47d6e74ed6a4441e6aeba6ac07", + "3559d4b574044e0e8bace41211b1649f", + "2b8042363eca490d9be41f7876e0aab0", + "a7ba9072d835401592f843ed6fe9a334", + "47bee386693546e4906a547ff5556cb2", + "14c1ae08e37343c683e4a8a3f8173d79", + "f5b15a86202840cca452980125b90c48", + "de3bbb53c6704b888d9b9754434bfdb9", + "ee7a0a507eae44e192b9d10ffdaf26de", + "a2798e676ae644a0bc091c23c1e7c5c1", + "ffc83e4385534117bc664c291338f355", + "5fdae6f5461e462dad3e5309fffe4cc3", + "db4f225255194d17bb18112c8a4d10e6", + "ea833033e1c24a8faf3bec5e29418b5e", + "b9c6b47990fe48aab0970677ab004b46", + "ea9cbfeffdc64559af9b768846b634a5", + "326423bae4c54057ad221e7c3077ad64", + "05cf4bc8861048029fcf6df306e7c8d0", + "c62a384e50324db581016107e3a22952" + ] + }, + "id": "gtHKG7PiGyyF", + "outputId": "5bd57d09-d663-4156-9df3-3725eeb33aee" }, "outputs": [], "source": [ @@ -759,7 +1045,7 @@ "\n", ":Zenkey rdfs:subClassOf :Zebra .\n", ":Zenkey rdfs:subClassOf :Donkey .\n", - ":Donkey rdfs:subClassOf :Animal . \n", + ":Donkey rdfs:subClassOf :Animal .\n", ":Zebra rdfs:subClassOf :Animal .\n", ":Human rdfs:subClassOf :Animal .\n", ":Animal rdfs:subClassOf :LivingThing .\n", @@ -773,6 +1059,7 @@ ":marty a :LivingThing .\n", ":marty a :Animal .\n", ":marty a :Human .\n", + ":marty a :Author .\n", "\n", ":john a :Singer .\n", ":john a :Writer .\n", @@ -780,15 +1067,11 @@ ":john adb:collection \"Artist\" .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "7ZeZNno5k3XW" @@ -796,7 +1079,7 @@ "source": [ "Cases 8 - 15: RDF-Star\n", "\n", - "`rdflib` has yet to introduce support for [Quoted Triples](https://www.w3.org/TR/rdf12-concepts/#dfn-quoted-triple), so ArangoRDF's support for RDF-star is based on [Triple Reification](https://www.w3.org/wiki/RdfReification)." + "The [rdflib](https://github.com/RDFLib/rdflib) package hasn't introduced support for [Quoted Triples](https://www.w3.org/TR/rdf12-concepts/#dfn-quoted-triple) yet, so ArangoRDF's support for RDF-star is based on [Triple Reification](https://www.w3.org/wiki/RdfReification)." ] }, { @@ -805,79 +1088,95 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 100, + "height": 167, "referenced_widgets": [ - "8787ef8386834db8a1fabdd1dd84fa53", - "0d91a444dc224f9db01cceb0a800e11b", - "5c28912b58204b69aca48cef391be3b8", - "9f1771b2fea94c3bac52f3834bebdce2", - "0304960babaf40e4a92bbbc118e10a24", - "5838a39a9e134909bc2a1c1d24052997" + "cbd2e6e8012d489da9a645a46a5c86e6", + "5fe9196bb6434bc4a410828e6d966c1d", + "1153c63c0eaa41ec8e041d18aa60bb1e", + "07dca4c78ef047919da9e857902cc9fe", + "f546c39ec32440478213a3d65d4bc56f", + "377819d3b19240fbb1afdbe5b5cc3d19", + "ece3c57da50247d28899dbc8f3178856", + "68a631ba14a04579a21076e608113f5b", + "3048cf5c63824a339a17d275ba6f5dbb", + "fbdd9315fee8499b9445d1d41ff052ba", + "5a1c4b8078e94b92ae912eeb9b9ee02b", + "fa0b56ec689c405d9903de685af4e39e", + "dd548245268443c9b84ad36e2b7659e0", + "e4e29c64dbaf484b9d4d2ee2e820445e", + "a49f012e93be46ddb68a33c2fce2760b", + "fab556deb44348ee88db08d6e4166484", + "09e6afa357fd4803be75a3378a16668f", + "2c2e6463d20f4b6ab097a09ee4a38b5a", + "00769c4ddffe45caaf3d6b25b36dbc1e", + "19970b3954634ea4b51383a554c939d6", + "df96d0217860475fb2115fd656c8e075", + "3ecde2ba048344a9877afe06fae66e02", + "cbf2ac2f71e24256bcae1e4321e39d58", + "88337681b0304b969bbad61c5ec0c3b4" ] }, "id": "XevGMv7qdPgI", - "outputId": "50495223-2b27-4d2e-e84c-01cc964b8432" + "outputId": "a562dae2-048f-4d80-8d28-e5ed1fdb0649" }, "outputs": [], "source": [ "# Case 8: Embedded object property statement in subject position\n", "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "<< ex:alice ex:likes ex:bob >> ex:certainty 0.5 .\n", - "\"\"\"\n", - "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", + "@prefix xsd: .\n", "\n", "[] a rdf:Statement;\n", " rdf:subject ex:alice;\n", " rdf:predicate ex:likes;\n", " rdf:object ex:bob ;\n", - " ex:certainty 0.5 .\n", - "\n", + " ex:certainty \"0.5\"^^xsd:double .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "KAs-MpmAp8_c" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84, + "referenced_widgets": [ + "b69a1ecf8f1447ddb4f04e55212f6d97", + "5f98553c1f4f4594a4b04d519700c077", + "b669c7e6ace146ba8a5b4a2acd29064f", + "da95283e26034646ad205320aa8900ad", + "eb609f79887b4c7b9c796b802290f645", + "3983a55e940b4729869ddcc63b83bb51", + "abdad6f869ba40529d63e6ca8d4b854c", + "09c18b9e1bfa46e297e2476df6de6349" + ] + }, + "id": "KAs-MpmAp8_c", + "outputId": "ef925d15-c87d-44af-fc8d-f879adb94592" }, "outputs": [], "source": [ "# Case 9: Embedded datatype property statement in subject position\n", - "# Note: PGT does not support this case \n", - "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "<< ex:Mark ex:age 28 >> ex:certainty 1 .\n", - "\"\"\"\n", + "# Note: PGT does not support this case yet\n", "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", "\n", "[] a rdf:Statement;\n", - " rdf:subject ex:Mark;\n", + " rdf:subject ex:mark;\n", " rdf:predicate ex:age;\n", " rdf:object 28 ;\n", " ex:certainty 1 .\n", - "\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)" ] }, { @@ -886,28 +1185,41 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 116, + "height": 184, "referenced_widgets": [ - "3fe6ce0b39d444f8aa1fa45b6b6f1aa7", - "9713b9bae1574359a4059ee19d26bbef", - "57e31b0d41514940b9cfcc5397d6ee2d", - "2f50e0986425434fa1ac6566e86cd574", - "6be66e4518f04ee197f3a89a23a63e73", - "17756af9fd3f455a9a9da6c02e4b358d" + "f500a92dca614fbe97190ea07b76f391", + "1980f083a8a24d118d4929daae64b05d", + "b4da93b0d8664c74bfffcd2fdf727d01", + "8182af5eea8247f38b41d468c10b2ce5", + "68ed047e78784348b8478e06f36498da", + "841e8ab2c5f044b4a1fadd8c2acd4a89", + "a32bca072f5c491cae9947d2b4cd5de9", + "6583d4fa4e8e465dbd8a6f7e903f9766", + "0e59fe845b1f474181c50e61c986780c", + "ba49567d52134f59be958023a178943e", + "5feded6877d94084a1426ceec3d0baca", + "31f235ab96ac4c3e866a1f42525bbf08", + "2da044ff861542359703ca9ae6c0d56b", + "a7a62d0a2adb4945bbff664966216d33", + "e91a8d9c045142c88600528a20845161", + "8de688d21cf147988f21e29a740ff08c", + "1b529365878647f980449bb9b38b9460", + "e34293eb4991417b9519a1029276803a", + "5a62d99ec49d4d2181f4296e97334516", + "4499a8f97cc4412db02dbe138ccf9357", + "2f40386b3fd1461b91d3809794504b5c", + "2d502c4cd2ce41cfa6e35eff82ebe098", + "51c97d40286f4bd9b922aedf3fd912a2", + "9134cccccc79418d887f13938c68d35e" ] }, "id": "_ZWrGS9Uqoc1", - "outputId": "24b962a7-76db-48e7-f05f-57f860d29c3f" + "outputId": "c150db3c-418d-45f7-ba26-0b5fb44a5eb6" }, "outputs": [], "source": [ "# Case 10: Embedded object property statement in object position\n", "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "ex:bobshomepage ex:source << ex:mainPage ex:writer ex:alice >> .\n", - "\"\"\"\n", - "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", @@ -915,17 +1227,16 @@ "_:x a rdf:Statement;\n", " rdf:subject ex:mainPage;\n", " rdf:predicate ex:writer;\n", - " rdf:object ex:alice .\n", + " rdf:object ex:alice;\n", + " ex:1 \"1\";\n", + " ex:2 \"2\";\n", + " ex:3 \"3\" .\n", "\n", "ex:bobshomepage ex:source _:x .\n", - "\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { @@ -934,29 +1245,42 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 83, + "height": 167, "referenced_widgets": [ - "f1aabec820b64be5b8e996a2a2c19643", - "51a410d1637f485494bbf71029df078c", - "bbd9f6489d6d4b4bacc62c3c743b09ee", - "d09e8f0f2980498ea82ee47e5487a0ac", - "1cd62a510adb45908e518c37d286a220", - "2f521f91fe8b4a05af9e5f492342b27a" + "0f9c78460dd242fb8aae8d90beb5de1e", + "7c318d14e6f34bfd8c415f6d3c395f1a", + "d2f6d27d78f943a38a86a253e8b445de", + "8998ed5dbfd44af287e21c401cf9d209", + "7713d7ed396c4fb3852c1513ae59a7a6", + "db06c3e74e6b4d0e87aba61e4386e521", + "ebd4df3b9f144ea18b91234936f1b6a2", + "6198ee915b6148259ac259d5d8709217", + "896ec3cb702c4e8693939179306da04b", + "fb7b8319d8c44e17b1e72f964c5a319b", + "7a9da186117340f18f557f39131f3ab3", + "811ac8febf1846ed9e2db9cfc2a6aeb7", + "31c785fe99cb45488b2370510428f2f9", + "9af45bf1246244c9bede98ed124371ae", + "2184f8202fbe4d359a0d611cdedb70c7", + "29ebc2c2cc8349c7ad0c33e8772a202d", + "f2ea318f46fa4045898a74adb4282073", + "d8f70e21fa5d4ef0a8185dd8537c8c42", + "ae2da01ed0d642abb114323e9f67d829", + "99b484013ed3422c9c5bd2d5bfcdbb47", + "c0e10fa9eeac4a66ab0a90c7b3c2fe80", + "ea6b9fdbcaa14135be29b6980706130f", + "f3738174a0b445429dc6942b109d7b23", + "3ec735ec0be8406e93b98c8fc5faa94d" ] }, "id": "E_iK33XDSiml", - "outputId": "cbd8922b-02fc-4137-b1d4-79c8881748d8" + "outputId": "584aa990-7441-40b5-edc9-a8bb86022143" }, "outputs": [], "source": [ "# Case 11: Embedded object property statement in subject position and non-literal object\n", "# Case 11.1: Asserted statement with non-literal object\n", "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "<< ex:mainPage ex:writer ex:alice >> ex:source ex:bobshomepage .\n", - "\"\"\"\n", - "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", @@ -966,14 +1290,10 @@ " rdf:predicate ex:writer;\n", " rdf:object ex:alice ;\n", " ex:source ex:bobshomepage .\n", - "\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { @@ -982,30 +1302,42 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 67, + "height": 184, "referenced_widgets": [ - "74986d09ef0142c7a5a7c21a3a5110ab", - "994b46fd0e0a446aa28211961585a578", - "465bc346393e44689437b9cc9077cd09", - "379e753ec1784adcb56e316c2b700d63", - "415dc3a7e28a47aeac3b8324e7fbe62d", - "c537bd80b0884e8fa3c156533ddd19ff" + "92282a6db94f41f79fe9e13b9354aadf", + "4a397ba17bd747a185a82cf1a1fb4637", + "223f64295def4d82b07989a0e9171191", + "eff7fd5f3f574ed38cfcafb5495e83c0", + "56658e42d18f4db7b09c3dff62b0e7cf", + "d7e349f506e34b12b72bc284c874c515", + "ce2a57c811794249afb7fa5c49af992b", + "b29eb50fc0bc4a7b9abf6a7da3042f72", + "f63d3988a9ce4174bd5e85f2f87b29b9", + "393a3b2b1fab47b3836ce742c9491594", + "10b486782c364980b39db66ee20b7834", + "82153626f6e34225908e5d49fa73d5b1", + "f98fd73455d24b718e6c0217cacc5ebb", + "d8ee71ebbae045fd82b67ed6fcb1d2ca", + "af5d50833aa74fec9e5916a7ee76fcac", + "666b5be3143e4d1aad23ead3f9ce53ea", + "2ff255c0ad7f4b6db1681395395073e6", + "5dc5238380194b4fb0726733020ce9a9", + "f12398082c0d4dd3961a166418f65f51", + "e097249a1e6c484f9d1c54e8e76bf5a4", + "aa947d9e955b476686905afb94bd9a30", + "ac20f1f535434e80b9db35fdf03f4c60", + "3735f9c2a9924655ae67398428eb9c14", + "e39bf2d80ac94271bdeabee0c0c3cbb1" ] }, "id": "0oZbDeLeS6ll", - "outputId": "7d370296-8c0b-4f5d-cefe-f05c820e58fd" + "outputId": "aae04df7-3caf-4278-f11f-5d0e1c243895" }, "outputs": [], "source": [ "# Case 11: Embedded object property statement in subject position and non-literal object\n", "# Case 11.2: Asserted statement with non-literal object that appears in another asserted statement\n", "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "ex:alex ex:age 25 .\n", - "<< ex:alice ex:friend ex:bob >> ex:mentionedBy ex:alex .\n", - "\"\"\"\n", - "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", @@ -1017,14 +1349,10 @@ " rdf:predicate ex:friend;\n", " rdf:object ex:bob ;\n", " ex:mentionedBy ex:alex .\n", - "\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { @@ -1033,31 +1361,42 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 100, + "height": 167, "referenced_widgets": [ - "b0083d492a3448e196aeb877f5ee2385", - "95116252d81e4a1aa9b344d96da31308", - "221d020caf68478ca7c1dcaf84b13f10", - "c529f4531a4242059f59383d7a111486", - "a74ca78dbcd44771892b708dd9705551", - "e1ffa38988d84dfe91f89d25c4f33535" + "510794a7ae37442f8b66a3a74661de66", + "c9855520f70e4202956e0d0c5dcfac69", + "63544062794940b8a60ec50c3d8fd5df", + "32c2bf3fe02f4b2b9f6e32cf8496a83e", + "a32dc9b3892f4087a3b824b3bab6486f", + "b1f35477806840a8b8a54799fe8e1398", + "60c5ddd64d9347f590e71a185604f117", + "49f447ea948b4d3cb29a19d0ac248815", + "1562b02158f14e2585764860276d7daa", + "2ca5014106714ad0a571daed220fe8a3", + "06f7a6f1943c403c96aca9b2be8cd920", + "cf53bc54ba0745639e93548d2d70a5f7", + "c9de6aa0f8464e86b5746365dff1bd28", + "0d9071e019d242b8835bd761a1e7ccb6", + "9e6876611736455caa8d7810419c28cf", + "d6e781982c5f44fcba3fbd679e951358", + "6ddbd53f84c04a808dbfda79308b5291", + "259234af91af41e5a00ff6dd69de6abb", + "9eac31d5717d4ca696ea683f4776987c", + "bce8c14036354838975daf20e8437b41", + "1eecac5b4e75476abeaea6ec99823841", + "8e496993198046a5ad9ecb6a7cfc358b", + "d711c89e86da452ba56a5d0fa177fc6e", + "bafb6c71b2174ce5a3b261d3c48cf0b4" ] }, "id": "woNfHiZ5S__t", - "outputId": "855406f6-c3b0-419e-a8c6-0d815c4cef22" + "outputId": "b547b01d-724e-4c65-de21-68ccb631ca48" }, "outputs": [], "source": [ "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n", "# Case 12.1: Asserted statement with rdf:type as predicate\n", "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "@prefix rdf: .\n", - "\n", - "<< ex:mainPage ex:writer ex:alice >> rdf:type ex:bobshomepage .\n", - "\"\"\"\n", - "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", @@ -1067,14 +1406,10 @@ " rdf:predicate ex:writer;\n", " rdf:object ex:alice ;\n", " rdf:type ex:bobshomepage .\n", - "\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { @@ -1083,31 +1418,42 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 100, + "height": 167, "referenced_widgets": [ - "a4dea9f78e014a9d9ddd35cc5b0256fb", - "2882bb947da84d6d8ebe063364cacfff", - "2823ee8e9ecf4ce2bdac9f641c3f037f", - "75b23a1142834c47843d9f451fea17d8", - "c84484cf3f6b4035963ef5de688027a9", - "e1d3b02470c3404fa5faf0b6a0a8acb6" + "31d64688e87848b5b582bf5c46dd4dd9", + "b4aeebce09de4f76b56757c293f765e4", + "82bb77ad205b41b18346d25c709bfb04", + "46356aae8ddc45ffa519a783411ed5c3", + "b1664273e12e42fe9ffc87283ee1ac17", + "3b77a9b7d5a54d0bb587666d26fe9638", + "9f871650a7f9452a972e3fb359f6202c", + "acc2faa48c3347c3b4f58272b0119048", + "95531688a8654c2fa47d740cd92b338f", + "2ebcb5a1f513410f8f703da16b3f44c1", + "de930ca00f7d4853afa6d2cd832ecd06", + "78ac12ef772441a49ab15c1a583efed1", + "7516668d476a4ad296e457ed2dce582c", + "f8e863f9e8d445b9a9f70a3ebe71cc22", + "f27e93a7fb054bc68b2030ed0d7a01a2", + "52d16d34612d42a5ace968d4104c89ed", + "03929fe528d24f6d8dca2511a18a02b1", + "6ae66ff0e0a84581b6c836c7b9df5472", + "8d7e37c4a725424cab94706f7c149b68", + "8c983236500b40628285ef1c5ffc4486", + "07ab0f2d3ee94fe0a63ce1df394192e7", + "47de40701c0e44b4980e966e2feeefe2", + "07a08a86cc2c4981beb1ff4d1715e4c0", + "f711293be6064fbeb78a10e36fb4ca3c" ] }, "id": "lbbdb2lwS_2M", - "outputId": "d9ae1108-1ba5-4dd9-b61f-8bae40cabe39" + "outputId": "0b252df3-43ed-4a2d-d459-888207d45bf9" }, "outputs": [], "source": [ "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n", "# Case 12.2: Embedded statement with rdf:type as predicate\n", "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "@prefix rdf: .\n", - "\n", - "<< ex:lara rdf:type ex:writer >> ex:owner ex:journal .\n", - "\"\"\"\n", - "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", @@ -1117,55 +1463,72 @@ " rdf:predicate rdf:type;\n", " rdf:object ex:writer ;\n", " ex:owner ex:journal .\n", - "\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)\n" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "4TwIzKZ4S_tN" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 167, + "referenced_widgets": [ + "86fb91142a524df2aea0b38e5e70ebf3", + "56fafa7b834944e8ab133cf454d7c975", + "1096a45ec72c4288942a61bfb522fcfa", + "99818b0b3b334807a9d480701be490ea", + "69d271c6000c484fbcae7d9c1df71c2d", + "aa4a6d44d69b4c3c9e14217ddf76fbe3", + "b90d6e06cf554533a8c4b2d0e7aead3a", + "5474b2535fdc4876932eb118f1f9da25", + "3a490438c9024452a8dc43ba4380184a", + "dce9a62edd304dd986fc904eaa2be8bd", + "e3ed2561d46c462492517def810ef87a", + "9133433c68334384a838e7bb43d46a72", + "ba1aa9d3a8414164b0a5f469e618a093", + "8aea416f223d4311a6cf9d4f1843df79", + "f8c653de7fdd4d28a0b4e855e31f8497", + "930632942d004d67aca96d6b1dd1f4f8", + "a98c3bdfb31e47d5b25442fa3114f5dc", + "2cf2703a60dd432a8cafa2d44cebc91f", + "d51d672e72484bf8abebd43741065c75", + "59c050129aa24f939162cf2a24329d1f", + "f5486bbcd6a542029db8c33230db6fbf", + "00aeacee663742bca19ea1564d49593a", + "5f1ab0c9d552407e8dd816c32ba3ff0f", + "76bd81b761b0409c89b9ce656b8bee27" + ] + }, + "id": "4TwIzKZ4S_tN", + "outputId": "b4f66a58-7857-411e-e900-faab135e9253" }, "outputs": [], "source": [ - "# Case 13: Double nested RDF-star statement in subject position\n", - "\n", - "\"\"\"\n", - "@prefix ex: .\n", - "@prefix rdf: .\n", - "\n", - "<< << ex:Steve ex:position ex:CEO >> ex:mentionedBy ex:book >> ex:source ex:Journal .\n", - "\"\"\"\n", + "# Case 13: Nested RDF-star statements\n", + "# Case 13.1: Nested RDF-star statement in Subject position\n", "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", "\n", - "_:x a rdf:Statement;\n", - " rdf:subject ex:Steve;\n", + "ex:steve_position_ceo a rdf:Statement;\n", + " rdf:subject ex:steve;\n", " rdf:predicate ex:position;\n", " rdf:object ex:CEO .\n", "\n", "[] a rdf:Statement;\n", - " rdf:subject _:x;\n", + " rdf:subject ex:steve_position_ceo;\n", " rdf:predicate ex:mentionedBy;\n", " rdf:object ex:book;\n", - " ex:source ex:Journal .\n", - "\n", + " ex:source ex:journal .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { @@ -1174,36 +1537,64 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 67, + "height": 134, "referenced_widgets": [ - "3b3582c5b8c34e6fa0aa8438adca7c94", - "be3545f15d0a484a98b0a049586fb907", - "8f7985fd0a84462eb451d85e428dbeff", - "a199384048dc44249882c3e0c65fda2f", - "b5d4fa7d28e143e2abd243f6846c6bef", - "536e9c1c2fbc456e8271519408c21f66" + "4c4e06460b1b45ab9cc9f4c17735e00c", + "5050f635e23d4d568a5ee162f3215cc4", + "36485dd1f0ac4020afb98ac853eed41c", + "6d22cce22d104fcfbac869f965502e53", + "b9ac6f8011d44f3396ba8b10c732253f", + "8d0d7952de7a4da4a759103ab22d2e37", + "c50644b32a9a416eb8590251c6fcbcb0", + "d988cd5ee4a34d2e8f86fef5093907ce", + "0ff7666935a04234b659ba79a2597c71", + "eff0b0606bdc40c88ebbd0509f4e8f14", + "4e9eb2e764d5451b9e583a3fc6290103", + "c35fb525a58246eebbf2fef1bd336b5e", + "e202d4e312114fae8ba6694151cfbbd5", + "f2b43ef11cf7421295d4c96b95c5e703", + "1ac2539ba6d94d6e92826d93d66dc649", + "f4d86a9622e74997aa7516410d0b7d92", + "aecca36ee5df464286c080f69a90a3b0", + "d7e12b91acdf4c68b11d967db6c533fa", + "fed1fa7af3a744d6959ca223c349806b", + "6dd871296cf44b4686c58701864c723c", + "635a63f420c848c089f70ca2e5aae701", + "816f7c8969da49e080916a9a6e6bf6df", + "b9f506c4c28a4e38be7b3b16cf972e06", + "05199d53ef264ae6acb67ef478a436ae" ] }, - "id": "yZDLiPMkS_kG", - "outputId": "97d98723-6c93-4860-cc8a-a829fee5f8dc" + "id": "BfMbv1NpP6nt", + "outputId": "23bacd02-4a05-4e7b-9955-860ab70d9801" }, "outputs": [], "source": [ - "# Case 14: Multi-valued properties\n", - "# Case 14.1: RDF statements with same subject and predicate and different objects\n", + "# Case 13: Nested RDF-star statements\n", + "# Case 13.2: Nested RDF-star statements in Object positions\n", "\n", "data = \"\"\"\n", "@prefix ex: .\n", + "@prefix rdf: .\n", "\n", - "ex:college_page ex:subject \"Info_Page\";\n", - " ex:subject \"aau_page\" .\n", - "\"\"\"\n", + "ex:steve_position_ceo a rdf:Statement;\n", + " rdf:subject ex:steve;\n", + " rdf:predicate ex:position;\n", + " rdf:object ex:CEO .\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", + "ex:book_mentioned_by_steve_position_ceo a rdf:Statement;\n", + " rdf:subject ex:book;\n", + " rdf:predicate ex:mentionedBy;\n", + " rdf:object ex:steve_position_ceo .\n", "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)\n" + "[] a rdf:Statement;\n", + " rdf:subject ex:journal;\n", + " rdf:predicate ex:source;\n", + " rdf:object ex:book_mentioned_by_steve_position_ceo .\n", + "\"\"\"\n", + "\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { @@ -1212,32 +1603,99 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 100, + "height": 150, "referenced_widgets": [ - "671b4473d89c4e7db560ecda1995cbca", - "5d82d19332db417998f81a504843e33b", - "96174346c25947fc9b1f7cee248378e0", - "b6e7e282065a4dc2b4efe8f72f2eaf31", - "fab828255ae74fa286ab18bdfa024314", - "5a7eb5c6b8a6441b956e9b778124e3b1" + "fc20c290802a41db8737daee15189174", + "41a6f15ee08a4e4e8ec03d1497b5ad2e", + "e6c4596dd3914ee2b7c338eff8bcf647", + "e58f215244e64ff68be337fd5a183058", + "cf37b65d12b54c17b1ed4c98f85bff2b", + "850743cb94c341849186c864f3d1f51c", + "934cbfecfbe34488abddbcd88f61ef26", + "de5ed27e132945f58444c0fb1a797f17", + "44bf2681ecea4978ac5e68449c2ebd2b", + "a5e9deebe6da40b889153e028b9a68bd", + "b817b83ccfed4b3daf48fdb2e0c33487", + "ebd854f567124326b5e790eeffde107a", + "d3d17d3949b6429a8a29397f99936256", + "feebddc745d8419fb64f0d144dc5c95b", + "0c39047fd29f488a8a731a3278b519b4", + "835443e97f3c4fdb98f0b1947e4dde81", + "c7d57948e351421a8cc2329cabdad6d1", + "f3fd59269d1740ef80c59a6dad9c2319", + "6b579a16241a49fe894af095168eab07", + "c4d12e6c75674af08b8adb827ab57615", + "0cb0cb6662834a5195ccf041f26e274d", + "105d4fe99189401994e333ed7978ebc3", + "39504ef3a8ed4dd285d67771f81f415a", + "f931874b27c84f2a9d8c0b6ef67f764f" ] }, - "id": "X_qBsxffS_br", - "outputId": "663faaf3-32c0-478d-a80a-50332feec396" + "id": "yZDLiPMkS_kG", + "outputId": "ffc977ba-aa38-42fc-9859-517e63cc453e" }, "outputs": [], "source": [ "# Case 14: Multi-valued properties\n", - "# Case 14.2: RDF-star statements with the same subject and predicate and different objects\n", + "# Case 14.1: RDF statements with same subject and predicate and different objects\n", "\n", - "\"\"\"\n", + "data = \"\"\"\n", "@prefix ex: .\n", - "@prefix rdf: .\n", "\n", - "<< ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 .\n", - "<< ex:Mary ex:likes ex:Matt >> ex:certainty 1 .\n", + "ex:college_page ex:subject \"Info_Page\";\n", + " ex:subject \"aau_page\" .\n", + "\n", + "ex:college_page_2 ex:subject \"Info_Page\" .\n", + "\n", + "ex:college_page ex:link ex:college_page_2 .\n", "\"\"\"\n", "\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 167, + "referenced_widgets": [ + "b133a98869b142fa817360896ddafa70", + "d38bbde094c7431289f7601a13315025", + "9c94473b37144abb9cbe6ac26b8b64a4", + "7c4cb95e53f345b08c1f4439d2f4c2b1", + "02782281dd414f71b683b9a32aa31caa", + "4888e641f7d1495487f59158aff1895a", + "e785484e947d4bce8dd4020c92d32681", + "eb48ef60fdf0417082c2c74dff515ef3", + "d0fb3aac531b474d868868ed2bdceaac", + "d6fbeecbce094ee681da46d7836b5501", + "862b42e51d1d4c1aac66d07fc07a1961", + "5344ab9f5213406f821b4fc52f34c1b6", + "d62264402f0940f9a3658d834661d80a", + "26ffe01988374a8ebcf74ed0c30773fc", + "4f1af8b21f1a4a8db470924b8030ac26", + "d3891a12da3149f78927bc97712eecba", + "603b1ecf2f3d4db78d8a0f290baca136", + "efd05d0c9db0475b86d43a4a7a269fe1", + "34766e8e7bac4be9bfb86c455ee75493", + "cd8d836cb70a42849cb48facc90fd6e7", + "9914c1cfebf6400aa9a8ac54763d80a3", + "1b37da3c0e884470af207ab618e4d83a", + "2a6a1086a40341b3a9fb46912cb75dd6", + "207a6a1c6f714ee9a9482d83aa1b6d29" + ] + }, + "id": "X_qBsxffS_br", + "outputId": "0ba71c7c-e1ec-42d0-b82c-72835fac14ad" + }, + "outputs": [], + "source": [ + "# Case 14: Multi-valued properties\n", + "# Case 14.2: RDF-star statements with the same subject and predicate and different objects\n", + "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", @@ -1247,21 +1705,93 @@ " rdf:subject ex:Mary;\n", " rdf:predicate ex:likes;\n", " rdf:object ex:Matt ;\n", - " ex:certainty 0.5 .\n", + " ex:certainty \"0.5\"^^xsd:double .\n", "\n", "[] a rdf:Statement;\n", " rdf:subject ex:Mary;\n", " rdf:predicate ex:likes;\n", " rdf:object ex:Matt ;\n", " ex:certainty 1 .\n", - "\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 167, + "referenced_widgets": [ + "9a5fe71de1ca4e949b1b61173089cd7a", + "176ab88312ca4473b876b9dd94260f35", + "25bf71951a7a46e8ab818832fb6d770c", + "fe3b2882a7ad4ff99044c20354c1c8c0", + "bb24c652631f41bcbe0f9461372f8061", + "b0aea30c1ae54e57aec64cfa3f5e0f66", + "c8a31feebbbd4479bb4d3149f703b482", + "4801a13b780346d888b36082869de287", + "f139ed160d5641fc8603a7e27f4739d1", + "1833ea424b6944919951e027db237691", + "d7f0e36535a7471c89e7992b8d1b7c01", + "70bb188603354ddca66d4146ec8e471c", + "960cca7f5fe44b1db8d6f1b42ec9c96a", + "d515e99b328d4e118174560fa329af02", + "32cbde21198749b880278b11b845eeca", + "99036c6715c544ea9c50cd68621fde3a", + "680bf32f724e469982385088a61bb1fc", + "29bd1e51d7a5416db6f8646bbf620040", + "13356c7a8f8b4a83bfe2caa420745097", + "a89b108c9fec490687b74e968dfe98f7", + "cdd0250b550f438c863d72f4706d9131", + "86fd50b8fd6f4dbf95b30557df477519", + "b2b5e1788189446fa4ecab95341c4c20", + "7e791f4fd0d54af68e2f96e3e3119830" + ] + }, + "id": "ZQqkFmPZQ69J", + "outputId": "2cc05095-f116-49e4-e2f0-deb47d721d25" + }, + "outputs": [], + "source": [ + "# Case 14: Multi-valued properties\n", + "# Case 14.3: Contexted within Named Graphs\n", + "\n", + "data = \"\"\"\n", + "@prefix ex: .\n", + "@prefix rdf: .\n", + "\n", + "[] a rdf:Statement;\n", + " rdf:subject ex:college_page_3;\n", + " rdf:predicate ex:subject;\n", + " rdf:object \"Info_Page\" .\n", + "\n", "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "[] a rdf:Statement;\n", + " rdf:subject ex:college_page_4;\n", + " rdf:predicate ex:subject;\n", + " rdf:object \"Info_Page\", \"aau_page\" .\n", + "\n", + "ex:Graph1 {\n", + " ex:college_page ex:subject \"Info_Page\";\n", + " ex:subject \"aau_page\" .\n", + "}\n", + "\n", + "ex:Graph2 {\n", + " [] a rdf:Statement;\n", + " rdf:subject ex:college_page;\n", + " rdf:predicate ex:link;\n", + " rdf:object ex:college_page_3;\n", + " ex:foo \"bar\";\n", + " ex:foo \"bar2\" .\n", + "}\n", + "\"\"\"\n", + "\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)" ] }, { @@ -1270,31 +1800,107 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 67, + "height": 167, "referenced_widgets": [ - "119b38887a7044c3adecda48e34a171c", - "da3cdc7228ca4857b9cc2059d172942d", - "fcb5ff7dbef141b587166e4907b43852", - "ea6f3e9d8e494193b08118820f53476c", - "ae84e2c9b2514e2f98af669dee2387b2", - "1e161fa8e44947dc8c4210c6db49fd90" + "9cee9150609e4c219279ed050a66a184", + "f3fe3d08163449909f5161eb0c8d3ce4", + "52d888e703e546578d3d9eb0d3cee5dc", + "1615255ac8434968865b3bcc80cb53b7", + "a60161904de345dab13563494719af43", + "8278b10ae8974c06811e7264527858d6", + "4800e6188bee433382b650145a17152e", + "4ba0cd3dd2be43d7b97e10804e01563a", + "c05911de0b4840c9a5cae04a58a58eec", + "e00e4a5e870c4ae2ab15e9ee4dafbfb3", + "752e8beefa234828be053813a24db5cb", + "bd7c88d2b8ac430ebe4d6e8146ad4a50", + "3ef4f135a9814fdd913d113eecc4cb2f", + "ee751b1aded24d2fa4c07f2779e2a049", + "754215a7023e452f9578907cf2b333f4", + "a5b9e7a95014425ab6963a48e86f49f9", + "6d47a7766b92481fb6ff95ba77e8f0de", + "690dc7e0e3ff44e98880bdc12d7d370a", + "cd435a80b43340cc81e55108f7c686b8", + "bddbfacd0981464fb4bc7c3380d9293a", + "20bc8e7e0c9842b4a46cc5fe0c81687a", + "36a9e23ee2454fbb907facd9a07f9fca", + "89fdce4ca2f94221892e9f0d1197b8e0", + "7f716d2ad0e34fe8a67b1f6b8a9669c5" ] }, "id": "cD-S3cZ-S_Ta", - "outputId": "c068301a-f806-442b-9995-ef77b5fff94d" + "outputId": "975b43e8-0151-44e2-cee8-34c160835cc7" }, "outputs": [], "source": [ "# Case 15: Identical embedded RDF-star statements with different asserted statements\n", + "# Case 15.1: Basic Reified Statements\n", "\n", - "\"\"\"\n", + "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", + "@prefix xsd: .\n", + "\n", + "[] a rdf:Statement;\n", + " rdf:subject ex:Mary;\n", + " rdf:predicate ex:likes;\n", + " rdf:object ex:Matt;\n", + " ex:certainty 0.5 .\n", + "\n", + "[] a rdf:Statement;\n", + " rdf:subject ex:Mary;\n", + " rdf:predicate ex:likes;\n", + " rdf:object ex:Matt;\n", + " ex:source \"text\" .\n", "\n", - "<< ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 .\n", - "<< ex:Mary ex:likes ex:Matt >> ex:source \"text\" .\n", "\"\"\"\n", "\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 184, + "referenced_widgets": [ + "00383ab1aadf438abf1f7ae40b0019d8", + "dd1262da5c7747bdb20dcfd9d0da8251", + "b60321f2d5444f349823a209b6592681", + "9abce425fb6a448a95b5b70d2d9a1f8a", + "92f7b68f2c954de1b9543accbb64f8c7", + "cc13170e10c142a89c2d36848ef13812", + "25200c81b79d4cf5b1887275467b25aa", + "dc71d23ca2c44ff39aad451bf7bb9bb8", + "fe07548f389642ecad02242a3297620e", + "ea218a29d344434abccaf303b0044dca", + "e492859f96704105b908c0f9e94f5aed", + "06d16a9226c94ce99cc1ba0de6828a0f", + "8b722d54416e4852b656d5cd0b2c5d08", + "d8b062e2e9da4af08c7e4d822f47c72e", + "ff0c11c9fd084a7790d7ae629772682f", + "b7e9590766f74bbdb62ea28a5bbc0ff4", + "da1a326b026644db9c1d3f956dbf35ed", + "465fbf12cec047dbb66c016aab1d876e", + "fceffadb163e4d74ae077a0e437bc879", + "c01224a7670240deb02a974dc360f0f2", + "89df658ca02348e5aef38d8b4158e772", + "eb1d608d4a384a19ae066051c956ce80", + "ab5bd1eda3b8465e8991c9dd80242069", + "8848879b486546d8b6b1039484813a7f" + ] + }, + "id": "r7jArqpvR6OK", + "outputId": "4d118087-03ed-4d19-a20e-73960dd22fd5" + }, + "outputs": [], + "source": [ + "# Case 15: Identical embedded RDF-star statements with different asserted statements\n", + "# Case 15.2: Basic Reified Statement + regular statement\n", + "\n", "data = \"\"\"\n", "@prefix ex: .\n", "@prefix rdf: .\n", @@ -1304,67 +1910,283 @@ " rdf:subject ex:Mary;\n", " rdf:predicate ex:likes;\n", " rdf:object ex:Matt;\n", - " ex:certainty 0.5 .\n", + " ex:certainty \"0.5\"^^xsd:double ;\n", + " ex:foo ex:bar .\n", + "\n", + "ex:Mary ex:likes ex:Matt .\n", + "\"\"\"\n", + "\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 184, + "referenced_widgets": [ + "1b7c320b49ba4a4d928090a55773d076", + "c5418daa64ef4c90ae44c7e845342cd2", + "5e5d6a1434204d9ca591ba7e5d80d394", + "eae1dc1f0c53488d9161546790aae34e", + "c0325b7f26d5436d99bee8a72fcd6d84", + "51c57fbccac34167855a2270c365b9b2", + "c0e5df890daa49eba05e2b3fc0c3a096", + "113d67880f1f4a48a88e1425b163cdb8", + "c05da93fed3d4d47a958f969dc575242", + "4c0629fb41e446bcb8c5bf7a6ed97820", + "0816edb1772f4e8f9e1326590f22a3cc", + "b2f03524128a4c6089d808686ad5281e", + "f334a52987ad4a0a9487c841b4eb4ab3", + "78620943d50549a3a57dbb4302b174be", + "d59751a9fa8045d884ebc30aeeadca70", + "7c21761ffbc04fd8a11c87bdcc07df64", + "580a7824847f45cd9ad774d975337480", + "1c533729c7f54f83b19169afb445d001", + "82aaa9fcf9ea489e89d567daf545bdf6", + "8a5cc8bcddb7487d872f8082c29bcdab", + "7f7008b7106847d0a71b51679e4e4522", + "5ad11cd62cc3452dbda9f543f100d135", + "b6eed34455224c21aa7b56e9222fcf44", + "3304bc08ecd648e6a566d74f17109680" + ] + }, + "id": "oWZcONtcSFwb", + "outputId": "16f97ebf-9a3c-4e16-dc1f-130c5a898287" + }, + "outputs": [], + "source": [ + "# Case 15: Identical embedded RDF-star statements with different asserted statements\n", + "# Case 15.2: Basic Reified Statement + duplicate \"regular\" statement\n", + "\n", + "data = \"\"\"\n", + "@prefix ex: .\n", + "@prefix rdf: .\n", + "@prefix xsd: .\n", "\n", "[] a rdf:Statement;\n", " rdf:subject ex:Mary;\n", " rdf:predicate ex:likes;\n", " rdf:object ex:Matt;\n", - " ex:source \"text\" .\n", + " ex:certainty \"0.5\"^^xsd:double ;\n", + " ex:foo ex:bar .\n", "\n", + "ex:Mary ex:likes ex:Matt .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 184, + "referenced_widgets": [ + "8ccc94cf504e40eab5c7ea493f756f8b", + "b36ff1e5657c40c19ed169b354198efe", + "97686950407044e493db9b6e5f96abc3", + "6d8ac4bfc9f6436f99c1e36a4d0d0e43", + "70df5da45fdf4c8da8a368073e139caf", + "d7f6a5acbacb4f95950ecb48616a024d", + "e68cc58160134aa99a339cf9d726fd8a", + "387e8f25a72745968de20c93174fb8b3", + "12e58e8320e6432f9e38fd28eabd7b6a", + "67d0366ca2cd42cdbc3392d5cd606bd7", + "f45d3a94b0df4882b4e1bbb4194d0e2f", + "a5ffe50661654304a6368878b509264c", + "f93c03087ce94becaee30819a8ccd195", + "ab184d09c37043dbb7218a9748f24b31", + "fc193d6dec664d4e8b54b59ce18c8909", + "6fbf0c9ea5c543e694559524ccde9489", + "92fa5df4e678468cb6562d228b0f9af8", + "6094952cc61f414cbb9682aa3fd33313", + "42c89ab89b204967a4970c9c6ca189a6", + "f90e31d881904ee1b8f31b089a3e95d2", + "907bb9cf62e74cd292ea26cd7cd8d205", + "503dd524d03a4032ab66b63d0de621ae", + "44d289bcd3eb4ebd91c1e4a4b327a1e1", + "fb584b5d386e4a7591bb48059cb7af87" + ] + }, + "id": "b_H6fDD0STPw", + "outputId": "61aebcda-78b1-4b02-daac-4d0ddd896062" + }, + "outputs": [], + "source": [ + "# Case 15: Identical embedded RDF-star statements with different asserted statements\n", + "# Case 15.3: Basic Reified Statement + duplicate Reified Statement\n", "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)" + "data = \"\"\"\n", + "@prefix ex: .\n", + "@prefix rdf: .\n", + "@prefix xsd: .\n", + "\n", + "[] a rdf:Statement;\n", + " rdf:subject ex:Mary;\n", + " rdf:predicate ex:likes;\n", + " rdf:object ex:Matt;\n", + " ex:certainty \"0.5\"^^xsd:double ;\n", + " ex:foo ex:bar .\n", + "\n", + "[] a rdf:Statement;\n", + " rdf:subject ex:Mary;\n", + " rdf:predicate ex:likes;\n", + " rdf:object ex:Matt .\n", + "\"\"\"\n", + "\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)" ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { - "id": "0SWi4e3wIMtw" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 167, + "referenced_widgets": [ + "d3cf60c5428e48d19db119ff4d747021", + "eae497edc98c4a2d81889d2cb4a3b621", + "3628924e22f54ca6bd3bc98dc0619de9", + "0b40cbc2604b4be58158cc8e56814f2e", + "ea632dc371e54437b52c5b22112b0e8e", + "bad0c11cb6ce497fb4c11bc03c533831", + "665e5cbce33143c0bc88c390aa35eed5", + "34df82f492d24cf18aa31027352e0df1", + "7d640d33c8a14846b089e21b404757a9", + "7e84fb9c3f464f08a4ba504cd7efdf0a", + "312d8f397d4f4362bbef7d18c1ba5de3", + "0d2b101b5cd34df5a60f49f0322b8ae5", + "27933cff78c94ea08128a4ab09aabc3d", + "5b5972f2a60d4165b29193acec5a3efb", + "c734fb7abf734de8a786b74490dbdc45", + "5483cddfc4a441bba4e38e06d531573b", + "14f9df9b0c98462aa5838e3da9fa1d9a", + "b5351fcc283f4f08a4c03ba7ff4c6171", + "23667a2c1edf48f0805b6f3a7deaff60", + "2d3b9ecb52c3494697f0914a9eda32ce", + "0b6cf37ac5ee4ab5a1d8b6dacd7cd5ab", + "733b2bb8ff1248dba07fb78f751141de", + "4de3bbd3d55a4b78a3eb1e521ce5cfe3", + "55577346bc664471a01442b07a9d5348" + ] + }, + "id": "FQj1859cRwms", + "outputId": "a8c7c579-7cb5-4f6d-d11a-13c661411d42" }, + "outputs": [], "source": [ - "#### RDF to ArangoDB w/ Graph Contextualization" + "# Case 15: Identical embedded RDF-star statements with different asserted statements\n", + "# Case 15.4: Nested Reified Statements with Named Graphs\n", + "\n", + "data = \"\"\"\n", + "@prefix ex: .\n", + "@prefix rdf: .\n", + "@prefix xsd: .\n", + "\n", + "[] a rdf:Statement;\n", + " rdf:subject ex:Mary;\n", + " rdf:predicate ex:likes;\n", + " rdf:object ex:Matt;\n", + " ex:certainty 1 .\n", + "\n", + "ex:Graph1 {\n", + " ex:mary_likes_matt_05 a rdf:Statement;\n", + " rdf:subject ex:Mary;\n", + " rdf:predicate ex:likes;\n", + " rdf:object ex:Matt;\n", + " ex:certainty 0.5 .\n", + "}\n", + "\n", + "ex:Graph2 {\n", + " [] a rdf:Statement;\n", + " rdf:subject ex:Mary;\n", + " rdf:predicate ex:likes;\n", + " rdf:object ex:Matt;\n", + " ex:certainty 0.75 .\n", + "\n", + " [] a rdf:Statement;\n", + " rdf:subject ex:John;\n", + " rdf:predicate ex:said;\n", + " rdf:object ex:mary_likes_matt_05;\n", + " ex:foo \"bar\" .\n", + "}\n", + "\"\"\"\n", + "\n", + "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n", + "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "vec21mb9MkhR" + "id": "0SWi4e3wIMtw" }, "source": [ - "Contextualizing an RDF Graph within ArangoDB is a work-in-progress feature that attempts to enhance the Terminology Box of the original RDF Graph. This is done by:\n", - "\n", - "1. Loading the OWL, RDF, and RDFS Ontologies as 3 sub-graphs via `ArangoRDF.load_meta_ontology()`\n", - "2. Setting the `contextualize_graph` flag to `True` in any of the `rdf_to_arangodb` methods.\n" + "#### Graph Contextualization" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "dUOCXzn5Owhj" + "id": "vec21mb9MkhR" }, "source": [ - "Enabling the `contextualize_graph` flag currently provides the following features:\n", - "\n", - "1. Process RDF Predicates within the RDF Graph as their own ArangoDB Document, and cast a (predicate RDF.type RDF.Property) edge relationship into the ArangoDB graph for every RDF predicate used in the form (subject predicate object) within the RDF Graph.\n", + "❗ Graph Contextualiztion is a work-in-progress feature ❗\n", "\n", - "2. Provide RDFS.Domain & RDFS.Range Inference on all RDF Resources within the RDF Graph, so long that no RDF.Type statement already exists in RDF Graph for the given resource.\n", + "Contextualizing an RDF Graph to enhances the Terminology Box of the original RDF Graph. This is done by:\n", "\n", - "3. Provide RDFS.Domain & RDFS.Range Introspection on all RDF Predicates within the RDF Graph, so long that no RDFS.Domain or RDFS.Range statement already exists for the given predicate." + "1. Loading the OWL, RDF, and RDFS Ontologies into the RDF Graph\n", + "2. Providing Domain & Range Inference\n", + "3. Providing Domain & Range Introspection\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "P9oGi91RJbAI" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0, + "referenced_widgets": [ + "b3891e36049d4b3ca529ee521cacb673", + "b63b93b6d74e4e008dc4de0268877371", + "6686917122224b15b7f511dc89212ce6", + "900b7c249fbb49c59c18d2c9968f2891", + "2a1d425b97524de6aac4edc3b8e67000", + "44ebf31be6c64bf4b539f81c1250a8f1", + "4d6a69a6ddd84ef68a4902e889af0e18", + "64938ff2e522484a94712608de674545", + "b1c9f3985d874907ad3038ad6a836afa", + "e077548daac5479a9092dd7985a85dc7", + "f586ae31f10141c7afe0f6baa1794d03", + "93ce4c6d2f2f43fa95a2a443f3321e4b", + "3e668d9f0ee74d078e3627b3ac969ede", + "f4bb379438d74bcc8c9312d491e37fa7", + "ba86d6019c8a4be19f4fb3afc63e0830", + "2a09643054274217b1166b0ad5db22aa", + "032a12f2cbaa4235aa24c1d127a28363", + "987b6a19cb0e4a29a79c4fc5596796c0", + "188785b1836c47d0bc6f01fba3524651", + "8a4170094368494aa860c215fffda76a", + "e62cf939fd2c47408098ba78de765f0a", + "291de7e0668e447c917b3a7134cae0fd", + "4eeaa3082d74487796caa0db21a1521a", + "9122d486ae7a422b96e097342891fb70", + "a71eeca8fd5746eea89964c6791c3b62", + "9275bb009a014e058d6b737d93d89c06" + ] + }, + "id": "P9oGi91RJbAI", + "outputId": "c9097281-026d-400f-f245-f4aae639ee62" }, "outputs": [], "source": [ @@ -1406,17 +2228,11 @@ ":Imagine :artist :John_Lennon .\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", - "\n", - "g = adbrdf.load_meta_ontology(g)\n", - "\n", - "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, contextualize_graph=True, overwrite_graph=True)\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, contextualize_graph=True, overwrite_graph=True)" + "rpt_contextualized_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), contextualize_graph=True, overwrite_graph=True)\n", + "pgt_contextualized_graph= adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), contextualize_graph=True, overwrite_graph=True)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "9gBg-hDs77i7" @@ -1426,153 +2242,739 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "_kkM4P0fWR4e" + "id": "KSGKrcaPa_pi" + }, + "source": [ + "We'll be using an ArangoDB-native dataset representing a Knowledge Graph constructed from the [GDETL Project](https://www.gdeltproject.org/).\n", + "\n", + "> GDELT monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day. [...] Put simply, the GDELT Project is a realtime open data global graph over human society as seen through the eyes of the world's news media, reaching deeply into local events, reaction, discourse, and emotions of the most remote corners of the world in near-realtime and making all of this available as an open data firehose to enable research over human society.\n", + "\n", + "The events we're using today range from peaceful protests to significant battles. The image below depicts the connections around an example event:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wtyqU1Wdb-jR" + }, + "source": [ + "![open_intelligence_sample 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IIAAAggggAABGT8DCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACASVAQBZQl5vJIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIEJDxM4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBQAgRkAXW5mSwCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAABGT8DCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACASVAQBZQl5vJIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIEJDxM4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBQAgRkAXW5mSwCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAABGT8DCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACASVAQBZQl5vJIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIEJDxM4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBQAiEBNVsmiwACCCCAAAIIIIAAAggggAACCCCAAAJ+LVDTXK2iugLlVGzV1vKNKq0rUnFDkcoaSpUckaKUyFQlRw3ShOSpGhY7XKnucWRItF/PmcEjgAACCHS/wIB2t3X/YTkiAggggAACCCCAAAIIIIAAAggggAACCCDQfQLlLgB7bsc/9db+t1VQm6/ipmLFRYcrIixMYWEhCgsJUWNzs5qaWtTQ1KSq2iYNjkhTWnS6Pp5xvs4f9l+KCYvrvgFxJAQQQAABvxYgIPPry8fgEUAAAQQQQAABBBBAAAEEEEAAAQQQ6N8Ctc01WrJ/oX677i7lNuS6UCxSaQPjNTApQSHBwYecfHNLi0rKypVfXKna+gaNix2nG6d8R5NSZriKsshDfo43EEAAAQQCQ4CALDCuM7NEAAEEEEAAAQQQQAABBBBAAAEEEEDA7wRyq3fr/1b/Um/uf1NpyYlKT0lUbEyUBgwY0OW5WAOtiqoaF5SVqai0Sp/O/rRunPp9JUYM7PIx2BEBBBBAoP8JEJD1v2vKjBBAAAEEEEAAAQQQQAABBBBAAAEEEPBrgXa1a0vJet343ldU1l6hKaOzFR11/FVfVdW1WpezWxlhQ/Sb0x7UsPiRfu3E4BFAAAEEjl2AgOzY7fgkAggggAACCCCAAAIIIIAAAggggAACCPSAwLt5b+iHy25WSFSLRmalKyoiotvOUl1bp5zd+xTVlqhfn/IbTU45qduOzYEQQAABBPxHgIDMf64VI0UAAQQQQAABBBBAAAEEEEAAAQQQQKDfC5TUFeoLr1+klohajRuZpeCgoG6fc2trq9Zv26WktjQ9cu4TigtP6PZzcEAEEEAAgb4t0P1/u/Tt+TI6BBBAAAEEEEAAAQQQQAABBBBAAAEEEOijAqX1Rfrue19XbUiVRmVn9Eg4ZlMPDg72jp/fsk8/XfZDVTdV9lERhoUAAggg0FMCBGQ9JctxEUAAAQQQQAABBBBAAAEEEEAAAQQQQKDLArbu2OM5f9eq8rUalZmu8LDQLn/2WHaMjozQmOzBemP/m3plz4vHcgg+gwACCCDgxwIEZH588Rg6AggggAACCCCAAAIIIIAAAggggAAC/UWgsrFcL+95QcMzkhUXE3VCppUYH6e0lDg9uf0fqmuuPSHn5CQIIIAAAn1DgICsb1wHRoEAAggggAACCCCAAAIIIIAAAggggEBAC/x+zd0qbS/W4NTkE+qQ7s63q36nHt38pxN6Xk6GAAIIINC7AgRkvevP2RFAAAEEEEAAAQQQQAABBBBAAAEEEAh4gfzqvXp01+NKS05QaEjICfWIjopQUmKc/r7j7yqrLz6h5+ZkCCCAAAK9J0BA1nv2nBkBBBBAAAEEEEAAAQQQQAABBBBAAAEEnMDq4hUaMGCAEmKie8UjJT5GDS31yqnY0ivn56QIIIAAAidegIDsxJtzRgQQQAABBBBAAAEEEEAAAQQQQAABBBD4j0BrW4sWFy1WcFCQ4mJ7JyBLSohTfVuDVpWu5roggAACCASIAAFZgFxopokAAggggAACCCCAAAIIIIAAAggggEBfFKhrqVNezR6lp8T32vCsei0tKU7byzervb2t18bBiRFAAAEETpwAAdmJs+ZMCCCAAAIIIIAAAggggAACCCCAAAIIIHCQQEtbs6oaKxUTHXXQOyf2aYxbi6y8oUztJ/a0nA0BBBBAoJcECMh6CZ7TIoAAAggggAACCCCAAAIIIIAAAggggIDU4los1jTXKDo8rFc5wt35KxrL3RiIyHr1QnByBBBA4AQJEJCdIGhOgwACCCCAAAIIIIAAAggggAACCCCAAAIfFWhub1Zlc7XCI8I/+uYJfMUCsrKmcuKxE2jOqRBAAIHeFCAg6019zo0AAggggAACCCCAAAIIIIAAAggggECACwQpSBFBYWprae1VCTt/RHBEr46BkyOAAAIInDgBArITZ82ZEEAAAQQQQAABBBBAAAEEEEAAAQQQQOA/Ai2tbcorr9Xm/EoFK0x1jQ29alPf2KSk8EQNcP/HhgACCCDQ/wVC+v8UmSECCCCAAAIIIIAAAggggAACCCCAAAIInGiBtvZ2tbS2q9Xdt7a1aX9FvbYXVynH3bYUVGq3C8dsGxBUrZrYAWpoaDrRQ/zQ+RpcQDYwLOFDr/EEAQQQQKD/ChCQ9d9ry8wQQAABBBBAAAEEEEAAAQQQQAABBBA4YQKtbe3aX1mnfRUf3PLc4wJ3K6lpVEltoxpcC8Pw4CClxERoYEy4zkmNU2pshBKi2vXPvCztrdio9LTUEzbeg09UVlWjGalDNGAAFWQH2/AcAQQQ6I8CBGT98aoyJwQQQAABBBBAAAEEEEAAAQQQQAABBE6AgAVgq3JLtTq3TKvyylylWLuscsyqxtz/a7ALwCYPSdSolDiNGhSvjMRoBbsAKshlUEHuPxZGWRy1r+1kPbB5uZqamhQWFnYCRv7hU9TXN6i+rkUnJc+gxeKHaXiGAAII9FsBArJ+e2mZGAIIIIAAAggggAACCCCAAAIIIIAAAscv0NDcqtKaBq8KrLC6QXvLarSntEa73K2svkkRIR9UhY11AZhVhGUlxmhYcrQyk2KVGNW1sGtG6ky1b2pXZU2dUpK69pnjn9n/P0JpZZUiQyI1NnHc/3+RRwgggAAC/VqAgKxfX14mhwACCCCAAAIIIIAAAggggAACCCCAwNEL7Cqp1sb8cm3Ir9AuF4jVuZCs3m5NLQp2lV/ZrhLstJGpmpCeqOyBsYoMDfZu4e4+6BhaFE5OnaH08FQVlVVqYEK8V1129KM+tk+0tLSouLxaI2NHaVjC6GM7CJ9CAAEEEPA7AQIyv7tkDBgBBBBAAAEEEEAAAQQQQAABBBBAAIHjF7DAq8pVgFXVN6vIVYjtKK7WtsIKbS6sUr1bLyzKhV1xkWGKiwhVRkK0xqXFa5RbN2xESqxiwkOPfwAHHCE0KEy3zPyJvrPsJtU3NCg6KvKAd3v2oVWtNde361vzvqeQIL4u7Vltjo4AAgj0HQH+xO8714KRIIAAAggggAACCCCAAAIIIIAAAggg0KMCZbWNWr+vTKvdemH7KutV6QKySheQ1TQ1K8itvjUmNVbzx6V7Idjg+CjFu4AsPjK02wOxziY5Z8iZmp40Q+v3rNS0sSPlLU7W2Y7d+Fq7WyhtR26Bzk3/mKakzurGI3MoBBBAAIG+LkBA1tevEONDAAEEEEAAAQQQQAABBBBAAAEEEEDgKAVc7qMG1zqwrqlVxW7dsFW5JVq6q1g7SmtdC0QpOizEtUQMUXp8pM4ZPVgThyRqrKsQO5b2iEc5tEPuHjQgWJ8beblWLF6p3fkFykofpAHH0K7xkCc46I22tnZt37tPUW2xunT0FQe9y1MEEEAAgf4uQEDW368w80MAAQQQQAABBBBAAAEEEEAAAQQQCBiBfRV12l5cqS2uTWJuea3yK+tUXNOoUJeKjR0Ur4umZmp4cqxrmRilQXFRrjKsb309ePLgubp67DX6w9b7FRURrtSBiT127fKLSrS/uEI/nHqrxiRO6LHzcGAEEEAAgb4p0Lf+BuybRowKAQQQQAABBBBAAAEEEEAAAQQQQACBPiVgFWJt7j9221FSrWU7i/R2ToEKXBhmVWBWJRblqsRmZyVr3qhBOikz+UR0LDxuo7DgcH1l0jdVXF+oJ7Y/oaCgICUnxh/3cQ88gNkVl5YpZ0+Brh9/vS4ZffmBb/MYAQQQQCBABAjIAuRCM00EEEAAAQQQQAABBBBAAAEEEEAAAf8WaGpp9SrCrEosp7hau0vtVqMiF4olurXCsgZGa2Z2ikamxLkqsRgNTYxWWEiwX076q5NuUGNro17c8byGD2lSxuCUbpmHrTm2e1+B9uSX6IpRV+hyV63GhgACCCAQmAID3F8K7ncm2BBAAAEEEEAAAQQQQAABBBBAAAEEEECgrwlYhdjqvaVasadEK3PLVFbfqObWdrW0tikxKkzzhg/SDFclNnpQnAvDghQaHNSr64h1p19Ta5P+uvn3+t2m3ykuLkKjstIV6douHutWU1unLXvy1VLXph9Mu0WfGH6Raz0ZeqyH43MIIIAAAn4uQEDm5xeQ4SOAAAIIIIAAAggggAACCCCAAAII9A+BlrY2FVU1aH9VvTbtr1BOUaVXKVbd2KIkF4alxEQo3a0dNjEtXqNS45XtqsSsnWJ/3prbmvTqruf16Pa/aVftDg0cGKNBSQmKjAxXSPCRq+NaWlpU29CoguIyVVY0aHTsOLfG2bWal3G2swvqz3TMDQEEEEDgCAIEZEcA4m0EEEAAAQQQQAABBBBAAAEEEEAAAQR6UqC4pkHLdxfrvZ3FynftE6samtXkKsQyXRg2OSNJU4YkKSspWjERoYoOD1FwPw/FOrMuayjRe/sW6I7Vv1D9gFqFhoQqKT7a3WIUGRau8PBQBbvAzAKxpqYm1blQrLSyRhXVdWpqblFaaJq+Pe0HmjHoZMWHJ3Z2Cl5DAAEEEAgwAQKyALvgTBcBBBBAAAEEEEAAAQQQQAABBBBAoPcErGWiBWCVdU1au69MK/e49om5pV5rxARXJZYWG6mZWQM1Y2iyhro1xfp7hdjRXomGlnq9nfuqXnW3HdXbVd1U5a1V1tDaoNagNgW3BysyyAVmwRGKC4/X+PhxOm/ofM0ZcpbCgo+9PePRjpP9EUAAAQT6vgABWd+/RowQAQQQQAABBBBAAAEEEEAAAQQQQMDPBSrrm7Qxv1yrXBiWU1yj4up6VTY2uyqxaK9CbNKQRK9KLNm1UYwIPXLrQD/n6JbhF9bmq6yxVHXNdaptqVVVc5Xiw+IVExytyNAoJUemerduORkHQQABBBDodwIEZP3ukjIhBBBAAAEEEEAAAQQQQAABBBBAAIHeFGh3J29qaVVDc6s2F1Tq/R2Fend7kVpd9ZiFX/GuVeLsrGSdOmKQxrj1xIKD+vc6Yr15LTg3AggggAAChxIgIDuUDK8jgAACCCCAAAIIIIAAAggggAACCCBwFAJ1TS3aWVytzYWV2rS/QnvKalXgKsVSY8I1OjVeY10YNnZQvIYmRivWhWRsCCCAAAIIINB7AgRkvWfPmRFAAAEEEEAAAQQQQAABBBBAAAEE+oHAzpJqvbU1X69s3i8LyWwLDQrSya5K7OwxaZqWmaywkKB+MFOmgAACCCCAQP8RICDrP9eSmSCAAAIIIIAAAggggAACCCCAAAII9LBAm2uTmFdeq52lNdqwr1zbiqq0o7RaMWGh3hpiw5Nj3ZpiiRqVGidbT4wNAQQQQAABBPqmAAFZ37wujAoBBBBAAAEEEEAAAQQQQAABBBBAoI8JLNlZpFc27dMWF4rVu/XFWtraNDktUaeOTNX0zIGKjwzz1hgLGsCaYn3s0jEcBBBAAAEEPiJAQPYREl5AAAEEEEAAAQQQQAABBBBAAAEEEAh0AVcopoq6Ru2vqteSXcVaurtY+ZX1Ghgdrky3htjJ2cmanJHkrSfWVavKykpVVFQoODhYgwYNUmjoodchKykpUW1trZKSkhQbG+udoq6uTva6fc4+H+TaOB7NVlRUpPr6eoWHhys1NfWIn29zAeDevXtVXFysdgeSkJCgESNGeOM/1HmPd4yHOm5vv27zr6qqUn5+vnddWlpaFBERocTERKWlpXmmvT1Gzo8AAgggcHQCIUe3O3sjgAACCCCAAAIIIIAAAggggAACCCDQfwVaXRCyw1WILdxepBW5JSqtaVRjS6smDk7Qf03J0uQhCUqKjlBkaPBRIzz44INauHChoqOjdcstt2jChAmHPMbf/vY3LViwQF/5ylf0iU98wttvw4YNuuOOO5SVlaXbb79dUVFRh/x8Z2/86Ec/0r59+zR06FDddtttSklJ6Wy3jteef/55Pfzww16oZwHR1KlT9atf/eqw512/fr3uvPNOZWdn69Zbb+0I9zoO6ocPLCh87rnn9Oyzz6qgoMALGVtbW71QzMJLm+t111132Ovph9NmyAgggEC/FyAg6/eXmAkigAACCCCAAAIIIIAAAggggAACCBxOoMG1Syx31WJr88r1Ts5+bSioVERIsNcy8bxx6TpjVJqGp8TqeFon1tTU6KWXXpIFK/v379f7779/2EClvLzc288qsnxbQ0ODF9BYwGahzdFsa9eu1YoVKxQZGelVoW3duvWwAZlVjv35z39WYWGhF8ilp6dr5MiRh616s/E0NjZ6n7HgyEI1f99sPvfee6/++c9/KiwszKu8GzhwoOdg18Ou06JFizzbX//615o9e/YRK/P83YTxI4AAAv1FgICsv1xJ5oEAAggggAACCCCAAAIIIIAAAgggcFQCha594qq9pVq6p0R7ympU7KrFhsRH6oKJGZqdnaIM10oxybVU7I7tiSee8FrzXXXVVfrrX//qhWX2+HBtFrvjvHYMC6ruv/9+r+Lpsssu05/+9CevImru3LmHPIW1crRwzMKge+65R4MHDz7kvge+MXbsWP3v//6vVzlmYZy/bxYsvvjii17Vn1WJnX322V5bRbtu1gLTgsTHHntMr776qn7xi194YZpVlLEhgAACCPR9AQKyvn+NGCECCCCAAAIIIIAAAggggAACCCCAQDcItLmgqKmlTTuKq/XKxly9mVOo4AEDFBYSpPGD4nXdqaN1yvDUbjjThw9hVWBPP/20YmJidO6558raEFo11yuvvKILLrjgwzv3wLPt27dr3bp1Gj58uC655BI9+eSTeuedd5Sbm+u1W+zslM3NzV61m1WrHSkcswBugHO0zdY2szkeafNVl/k+1937H+l4XX1/yZIlXhA2b948ff7zn++Yp33eruf48eN1/fXXa8uWLdqzZ49WrVqlwwVkRzvvro7z4P26cp4Dr9vBn+/s+dHu39kxeA0BBBDoSwIEZH3pajAWBBBAAAEEEEAAAQQQQAABBBBAAIFuF6hpbNGWggqtyi3Txv3lynEBWXxEqKsSS9a0jCRNTE9UZlKMgoM+CHm6ewAWmlRVVXnBSVJSkv7rv/7LC1IeffRRryLpaNcSO5rxWajx3nvveS0ZR4wYobi4OM2fP98LyR555BH98Ic//FDoY5Vjy5Yt07Zt27zTWJWUrUVmQdapp57qVaNZe0g7lq1l9sYbb3hVVBa+nXPOOd48V65c6Z3n5JNP9toS+sZrbSEtrLP3rTotJCREaWlp3nE7C+Gqq6u1evVqr61kUVGRLLSzUGry5MmaPn2693nfsXvqvqKiwjv0oEGDPuR04PksFDzllFO8yjJrw9jZZp5Wjeab95AhQ7x5ZGZmfui41oLTWnEGBQXpYx/72EfmaG0d7XrW19frjDPO6Fjjbc2aNV7gOXPmTDU1Nemtt97ygr1JkyZpzpw5HccpKyvT4sWLvTDP9rOfR/O0/YKDP7qunu1vP78Wptr1MIfRo0dr2rRpnU2T1xBAAAG/EiAg86vLxWARQAABBBBAAAEEEEAAAQQQQAABBLoqUFzdoNc25emNrQUqcWuMtba1Kz0uUlfNHqEzRw9WYlR4j4VivjFaQLV06VJZsGHBggUSEydO9IKhffv2eZVdtm5VT212Xgu8bBwWalhrwDPPPNMLvWxc+fn5srDGt+3evVt33XWXbM0024qLi/Wzn/3MC2wefPBBL6Sy59aq0cZvlWgWfFm4YqHVjh07dMcdd3jrlU2ZMqUjILN9Hn74Yf3lL3/xAhwbj20WBP3ud7/Tj370I5122mnea/YfC5S+//3ve+uw2b4Hrrlmwdp5552nW2+9tdNQp+Mg3fDA1l6zzdYZMyvf8wMPbXO/4YYbPGObz4Gb+T/00ENeG0YLv3zzsP0SEhK8OVjw6NssBDRfC9rOOuusjmDL975dF2uXaYHhhAkTOgIyC9Xs9q1vfUsPPPCAd/3MzdaNswDMzmUBnb1vx/CNw4JP87zooov07W9/23ca797CMZtXTk6Ot78dz/a3+X7mM5/x3jtUIPihA/EEAQQQ6KMCBGR99MIwLAQQQAABBBBAAAEEEEAAAQQQQACBoxewdcXW7SvXop1FrlqsQg0trcp21WGzh6VqemaSJrhqscjQj1bKHP2ZuvYJCzys4so2q9yygCElJUXjxo3Tm2++6b1nlVb2ek9sFups3LjRW3/Mt+ZYtlsjy6q/rCWgVWgdGJANGzZMN998s9cy0NZKs+qor3/9697QMjIytGvXLi8sWbBggUpLSzVr1izFx8d7QZdVgVlAZuGLL4CxD1qwYut0/fGPf/TW8rr00ku9MM3mbMd57rnndOedd2rMmDFehZJ91tbzysvL80Iiq0wzM6vCs7Dv5Zdf1r///W+ddNJJuvDCC3uCreOYVqVlFXTmeM0113jBnAVTZmhuvuq/zqqv7CDWWtPmbpV7NlYLq6xyy1ps2jxuuukm3XLLLV6rTd/PwMF+HYNxD3xh4YG+9r49twDOQkgL3ywEtecWkFnVnf0M2HW1yjML3ux9C2stiPznP/+pxx9/XMnJybriiiu8z1ul37XXXquWlhaZgV0DC9nsdfN46qmnvKDMAjc2BBBAwF8FCMj89coxbgQQQAABBBBAAAEEEEAAAQQQQACBDoHimga9tD5Xb20rUGWDWz/LVYudMyZNZ41NV1ZitGLDQ90X/z0TQnUMopMH1g7Pgh4LpCzQsc2qbix0sIDMKrCsSseqeHpie+KJJ7xQxNY6s7DLNgtCbCwWdliLxE9+8pMdpx44cKC3hlhsbKwsILP7888/v+N9C8hss+oxaxVpAY/Nx0Kfg6unfB+yqjSrErPtl7/8pXduX6BkVWcW5FjoYi0fv/e973mh4aZNm7y2f7fddpsiIyN9h5KFfPbZf/zjH1q4cKEXLB3qvB0fOo4H1jrSqtusWs2qtqwtplXhmYsFgxZ4mZ/dHzwOCyCtesxC0p/85CdeKOibt1XL2c/EPffc483FgkZrN3k8m4Vk1iLz7rvv9oJLX5hmP1sWglmgadVqNhef6YwZM7yfh9tvv91bE+/jH/+4F4SZr1WaWahnlWW+/e16nX766V4FoQWbn/3sZ2XBKRsCCCDgjwI98zevP0owZgQQQAABBBBAAAEEEEAAAQQQQAABvxKodkHYzpJqvZtToEW7itXY0qb0+EgXjA3W2S4YG5IQ1avzsWDEAhLbLr/88g+FYPPmzfMClv3793uVRBZgdfdmgYi13bMA68orr+w4vFUq2fmsusnaLNr6UhbWHO325S9/uaOC6nABn52jsbHRC22s7aIvJLLz2di+9KUveYHT2LFjvSFYZdb//M//eFVavmDGNzYLoex9m4Md08K1g4Mp377dcW/HtlDIqsCeffZZL7yzoMxu1oLQAkML92yfr371q16o5xuP2dsablaxZQHYgZsZnHvuuV44tnPnTu84xxuQ2fEtqPNVCvrOZ04W1Np2/fXXd4RdvvctJLOqPltfzK6Hzcsq9eyaWgh64DUwd6sUtJ/n3//+917FmgVubAgggIA/ChCQ+eNVY8wIIIAAAggggAACCCCAAAIIIIBAAAvUNbXo7a379bq77ausU01ji2ZnJWv++CEamRKrxOhwBbkv8nt7s7Z2VqVlFVsWkNh6VL7NAhKrzrEqLQsaLCyJiIjwvd0t96+//roXIlkoZS0KDzx/VlaWt4bVunXrvOqun//850d1zujo6I6KtCN90Nou2mbtGzsL0iycu/HGGzsOY1VbdrPNqqIs6Nu7d68XIm3evNlbS8veswopux1uq6ur09atW70Kt4P3GzFihHdtDn69s+fWIvGLX/yiFyT5ArL169d7wZMZWstEq3778Y9/LN+acnb9bTtwjbEDj23VejZPC0mtyu6UU0458O1jetxZ0GkhXnV1tVcZNmrUqI8c11ot+tpo2puVlZVeSGa21oLT/A7eysvLvZd8czz4fZ4jgAAC/iBAQOYPV4kxIoAAAggggAACCCCAAAIIIIAAAgEu0OSqw0pqG7xqsZc27FNFQ5PiXNvEGUMH6sLJmRqTFt+nhCzYsfaJtlnbu09/+tOHHJ8FLosXL/bWhTrkTkf5hgVDvqqhtWvXem3xDnUIa1VoAVRmZuahdvnI61bF5auU+sibB7xga1hZFZVtvrXKDnj7kA8LCgr0r3/9y6vasoom2yxcszW/rIrpSMGY78C2dphVo9k1OHiz1okHtpc8+H3fc7uWvrmGh4d71XYWRFnVmFW/rVq1Sj/72c88Q2tlaAGZVbZZZZht6enpvkN95N4XaJl/d2xW3XXwVlhY6L1kFWJd2czMqh9tu//++w/5EQt5rfrQ9rW2k2wIIICAvwkQkPnbFWO8CCCAAAIIIIAAAggggAACCCCAQAAJNLW0atXeUr27vVAbCypla42NGxSni6ZlaVpGooYkxiikF9YWO9IlsPWbLDixzap2rALp4M1CHgsYiouLvTDNWuN1V9BgVUnbtm3zQqVx48Z5rfMOPr+FVxbiWID1/vvve8GPhU9d2bo6TguWrG2fbXY+m/ORzmHVVDfffLM3Ngt15s+fr+zsbK+1ooVNVrF13333dWWYXlXemDFjvPaBB38gISHh4Jc+9DwnJ0fPPPOM12Lwm9/85ofeO/CJred29dVX67bbbuuobrM5+ioCrcXhoTZfeOgz8u13qADQXrfA7lBbZxV6tl6abYcbx4HH8x3DWivadTjS9TrS+wcem8cIIIBAXxIgIOtLV4OxIIAAAggggAACCCCAAAIIIIAAAgh4IUpLW7vec6HYYyt2eW0Ug10INnJgjL522hjNyk7p80pWEbRlyxYvoLrzzjsPWUX0+OOP6+6779bKlStlbetSU1O7ZW5WPWbHs6qwBx98sNPWhlbl9JOf/ET//ve/vWqziy66qCPM6pZBuINYQOarXLLxdBaQWbXbggULvLWtbA2tO+64Q9aW0Sq07rnnHlnV1oGbtVm0kMiOdaggybd/RkaGfvOb3/ieHtW9VUY99dRT3mcuvvjiQ15D28HaWNpmbSxtTDZva5+Yl5fnVZZ11mbR9tuzZ4/3OWvDaZuvUs2uTWebvX5gq8zO9jn4NWslaZtVktk5Dw607LUXXnjBq/Azc1sLzcwt0Dz55JNlrSAP3szfxmLjPXBNuYP34zkCCCDQlwWC+vLgGBsCCCCAAAIIIIAAAggggAACCCCAQOAItLgv3bcUVOhvS3fopqeW6ddv2hpO7frkxAzd/slp+uVnZvpFOGZX7G9/+5sX4syZM+ewwYoFJ1axYy0F33333W652FYpZO0Jbfv85z/faThm71mwceaZZ9pDryrLF9Z4L3Tjf3wBja3Z1VkV06ZNm3T77bd763dVVVV54Zid/oorrvhIOGZhjq2LZZs9tltPbSNHjvSq6uz4f/nLX1RfX9/pqSwoeu2117z3LBTzBVAWLtn20ksvdRpq2fp0FqRapdb48eO9fe1nwarzLICySrqDN1vzy9YIO5rNWltaUGjjtyDy4G3fvn36wx/+4LWJtIpGG4+1arR5LVu2rFNj8/jKV76iP/3pTwcfjucIIICA3wgQkPnNpWKgCCCAAAIIIIAAAggggAACCCCAQP8VWJdXpp+8tEb/8/xqPbF6j6oamvXfc0frrotm6bq5YzQlI0lhwf7xVZat4fT22297F8tChMNtFlz4qosefvhhL5Q43P5dec+qxyz0sNZ6n/rUpw77ETt3YmKiF+D0VNhhIdywYcO84MuCmANDLQttfvGLX3jzvvTSS70x+6qodu3a9aGx2+esGs+3tpsFOIdrN/ihDx/DE2t7eNlll3lVUlZhZSGerRd38PbYY4/pkUce8YKx8847r+Ptz3zmM7JwcOvWrXrggQc61vXy7WAhk4VdZmPBmm0292nTpnmPrWWjby0we8GCRKv4O9DP27EL//niF7/oHfv3v/+9F8Ye+JFXX33VW6PNfhZtTTT7eTjjjDM8W5vXweu3LV26VA899JAs8LSKMzYEEEDAXwVoseivV45xI4AAAggggAACCCCAAAIIIIAAAn4sYHU/hZV1Wpdfobe37deafeVKjAzTpCGJOnPUIM0elqqI0GC/m6GFFy+++KIXLtjaV6NHjz7sHKza6LrrrvPWALO1yN58800dGLIc9sOdvNnU1KTnn3/ee8eCqYPXtjr4I1atZOtn3XXXXV51kVWRZWVlHbzbcT23c1iwY+tZPfroo17ryVmzZsnGavO1NoRnn322V+1mHl/4whe8NcZ+97vfydZSM0ersLM13dasWeM9t+orW7/L2jNGRUUd1/gO9+Hzzz/fC/bsmr7++ut64403ZO0QLfiqrq72WiTaenM2x3PPPVcXXnhhx+GsGuz666/XL3/5Sz3xxBPecaZOneqFXosWLfKCMwukvv71r3uhlO+DX/3qVz0jCwivvPJKzZw5Uxa62vxtPbuKigrvuW//rtzPmzfPa6O5cOFCfeMb35A9t3XxrF2lhbk2p5tuukkxMTHe4SysXLt2rVdZaFWItg6crf9mFWZmYD/n1pLT1l9jQwABBPxVgIDMX68c40YAAQQQQAABBBBAAAEEEEAAAQT8VKC+qUXPrd2r17fuV0lto6JdEHbJ1CydPjpNg+OjFOmHwZjvUliLwCVLlnhP586d63v5sPcWekycONFrHWjrgZ1++umH3f9wb1rLvpycHC8Y6+r5LQT6i6tmKi0t1csvvywLaLp7s5DL1lqz9cBWrFjhrblm54iIiNC1114rW+PL15rwc5/7nDd+q3ayYMk2e8/Cxl//+tdelZMFTxas2bpavvW7vB27+T/R0dG64YYbdNZZZ+nvf/+7d20tyLSbbxs7dqy+9KUv6ZRTTvlIWGcVevfdd5837+XLl3stC+1zVilmYZpVdllIduA2YcIE3Xrrrd5c7VrazdoefuITn/DaTv70pz896oDMjOyYFp7atbYWoL7NwrKvfe1rXujne832t2q9Z599Vn/96187roO9b2uUWchmoabvmvk+xz0CCCDgTwIDXNpvv7DDhgACCCCAAAIIIIAAAggggAACCCCAQI8JtLa1q6i6Xot2FenFtXkqb2hSSkyEzhk7WOeNS1diVHiPnZsD9y0Ba6toFWBWdZWdne2FZJ2N0KrDrGKpoaHBa0NoFU+9vVm1mAVz5eXlXohnVVW2XldXNpvPzp07veorq9LrynyscqysrMwLB49UDdiVMdg+1prS5mCVaJmZmR+qXjvUMaySz9p2pqamevO1a8eGAAII+LsAAZm/X0HGjwACCCCAAAIIIIAAAggggAACCPRxgbzyWr28MU/L9pSqwIVkwxNjdP7EDJ00NEmpcZF9fPQMDwEEEEAAAQT6owABWX+8qswJAQQQQAABBBBAAAEEEEAAAQQQ6GWBFlcxVuzCsNc37dPz6/PU7JoYJUeH69OTM/XJyR9uKdfLQ+X0CCCAAAIIIBCAAqxBFoAXnSkjgAACCCCAAAIIIIAAAggggAACPSXQ5oKwbYVVenXzPq3JK3NtFRs0aXCCzhqb7lWMJbu2imwIIIAAAggggEBvC1BB1ttXgPMjgAACCCCAAAIIIIAAAggggAAC/UQgv6JOjy3fqTdzCrwZpcdH6erZIzVnRGo/mSHTQAABBBBAAIH+IkBA1l+uJPNAAAEEEEAAAQQQQAABBBBAAAEEekGgsaVVW13F2Jtb8rVkT4nqmlq8irFzxg7W9MxkxUeG9cKoOCUCCCCAAAIIIHB4AVosHt6HdxFAAAEEEEAAAQQQQAABBBBAAAEEDiGweX+Fnli1W+vyy1Xf3KoJafH63IzhGu9aKkaGBh/iU7yMAAIIIIAAAgj0vgABWe9fA0aAAAIIIIAAAggggAACCCCAAAII+I1AbWOLdpdW68UNeXp/Z5HCQoI0MiVOF0zM0IysZEUQjPnNtWSgCCCAAAIIBLIAAVkgX33mjgACCCCAAAIIIIAAAggggAACCByFwOIdhXpp4z5tK6pSg2uteLILxM53wdiY1HhFh/M101FQsisCCCCAAAII9LIA/3Lp5QvA6RFAAAEEEEAAAQQQQAABBBBAAIG+LNDgWifuLKnWY8t3alVemWJcEDYiOVafnZ6tKRlJChowoC8Pn7EhgAACCCCAAAKdCgxod1un7/AiAggggAACCCCAAAIIIIAAAggggEBAC2wuqNC/1uzV+v+sMTZvRKrOHjtY49ISFB7CGmMB/cPB5BFAAAEEEPBzAQIyP7+ADB8BBBBAAAEEEEAAAQQQQAABBBDoTgH7Teqy2gY9t3avnlmb6yrEpCEJUbp+7lhNykjszlNxLAQQQAABBBBAoNcEaLHYa/ScGAEEEEAAAQQQQAABBBBAAAEEEOhbAiXVDXrPrTP2yuZ9yi2v01i3ttjHJw7RjKwUxUeG9q3BMhoEEEAAAQQQQOA4BKggOw48PooAAggggAACCCCAAAIIIIAAAgj0F4GFOYX6y9LtKqiuV3x4qC49KVufnJypYCshY0MAAQQQQAABBPqZAAFZP7ugTAcBBBBAAAEEEEAAAQQQQAABBBDoqkBjS5vW5JbqlU37tHRPiZKjwzU7O1kXTB6qoYkxXT0M+yGAAAIIIIAAAn4nQItFv7tkDBgBBBBAAAEEEEAAAQQQQAABBBA4foHdpTV6eNE2bSyoVH1zq84ZnaZPT83S0KQYhVA1dvzAHAEBBBBAAAEE+rQAAVmfvjwMDgEEEEAAAQQQQAABBBBAAAEEEOg+gfb2duVX1uu1jfv0+tZ8NbgKsnGD4nXhlEzNcpVjbAgggAACCCCAQKAIEJAFypVmnggggAACCCCAAAIIIIAAAgggENAC5XWNetUFY69t3a/CqnqNSI7VZTOHa/zgBMVFhAa0DZNHAAEEEEAAgcATYA2ywLvmzBgBBBBAAAEEEEAAAQQQQAABBAJIoLapRWvyyvTnRTnaX12vxMgwXTBpqD45caiiw/nd6QD6UWCqCCCAAAIIIHCAAAHZARg8RAABBBBAAAEEEEAAAQQQQAABBPqTwKIdRXp50z6tzy/3qsTOGDlI545LV4ZbZ2xAf5ooc0EAAQQQQAABBI5SgF8TOkowdkcAAQQQQAABBBBAAAEEEEAAAQT6soBbZkx55bX6y+IcLd5TopCgAZqVmaxr547WoLjIvjx0xoYAAggggAACCJwwAQKyE0bNiRBAAAEEEEAAAQQQQAABBBBAAIGeFSirbdRbWwv00sZcFVY3aHZ2suaPH6IZLiALdkEZGwIIIIAAAggggMAHArRY5CcBAQQQQAABBBBAAAEEEEAAAQQQ6AcCO4qrde87m7Xd3adEh+uaOaM0a1iKQoOD+sHsmAICCCCAAAIIINC9AgRk3evJ0RBAAAEEEEAAAQQQQAABBBBAAIETKlDsKsVe2ZSnZ9flKsyFYTOzkvW56cOUnhB1QsfByRBAAAEEEEAAAX8SoMWiP10txooAAggggAACCCCAAAIIIIAAAggcILBkV5H+tGi7a6dYr2GJMfrSnJGamJ5I1dgBRjxEAAEEEEAAAQQ6E6CCrDMVXkMAAQQQQAABBBBAAAEEEEAAAQT6qEB7e7uKaxr0zKo9enVLvqLCQnTOmMH63MzhigwN7qOjZlgIIIAAAggggEDfEqCCrG9dD0aDAAIIIIAAAggggAACCCCAAAIIHFKgzYVjr2/K17/W7VVeRa3XTvHymSOUmRRN1dgh1XgDAQQQQAABBBD4qAAVZB814RUEEEAAAQQQQAABBBBAAAEEEECgTwlYMLa/ol5/X75dC3cUKyY8RF+YMUzzx2coLCSoT42VwSCAAAIIIIAAAv4gQAWZP1wlxogAAggggAACCCCAAAIIIIAAAgErUN3QrLe27tfz63PdWmMNmj82XZ+clKFhybEBa8LEEUAAAQQQQACB4xWggux4Bfk8AggggAACCCCAAAIIIIAAAggg0EMCBVX1+sUr65RTUq30uEh9de5ozchO6aGzcVgEEEAAAQQQQCBwBAjIAudaM1MEEEAAAQQQQAABBBBAAAEEEPATgbLaRr3gKsascqzVtVecMyxVF5+UrdTYCD+ZAcNEAAEEEEAAAQT6tgAtFvv29WF0CCCAAAIIIIAAAggggAACCCAQYALr8sr08OLt2u6qxkYmx+ibZ45XZlKMQoIGBJgE00UAAQQQQAABBHpOgAqynrPlyAgggAACCCCAAAIIIIAAAggggECXBQoq6/XK5n16avUepUSH6+MThuhjEzIUGxHa5WOwIwIIIIAAAggggEDXBKgg65oTeyGAAAIIIIAAAggggAACCCCAAAI9ItDmWigu21WshxblqKimQfOGp+hzM4ZraFK0ggZQNdYj6BwUAQQQQAABBAJegAqygP8RAAABBBBAAAEEEEAAAQQQQAABBHpLoKq+Sc+vy9Vzbr2xOFcp9jm3ztg549I1gGCsty4J50UAAQQQQACBABGggixALjTTRAABBBBAAAEEEEAAAQQQQACBviWwpaBSf1u2Q+vzy3XKsBRdPnOEVzXWt0bJaBBAAAEEEEAAgf4pQAVZ/7yuzAoBBBBAAAEEEEAAAQQQQAABBPqoQHNrmxZs3a8/upaKLa694mXTs/WpyZkKCwnuoyNmWAgggAACCCCAQP8ToIKs/11TZoQAAggggAACCCCAAAIIIIAAAn1UIL+iTk+t3q03XEA2blC8W2tsmKYNHdhHR8uwEEAAAQQQQACB/itAQNZ/ry0zQwABBBBAAAEEEEAAAQQQQACBPiSwNq9Mt7y4Wm1t7bpgQoaunTdawUFBfWiEDAUBBBBAAAEEEAgcAVosBs61ZqYIIIAAAggggAACCCCAAAIIINALAqW1jXp5Q55e2pin+IhQXXzSMJ0xOk0hQQN6YTScEgEEEEAAAQQQQMAEqCDj5wABBBBAAAEEEEAAAQQQQAABBBDoIYHc8hr95q3N2lZcpRmuleINZ45XXGSoggYQjvUQOYdFAAEEEEAAAQS6JEAFWZeY2AkBBBBAAAEEEEAAAQQQQAABBBDoukB9c4ve2lKgJ1bvUqtrqXjhpKE6f2KGYsJDu34Q9kQAAQQQQAABBBDoMQEqyHqMlgMjgAACCCCAAAIIIIAAAggggEAgCpTWNOqRpdv1zvZCDR8Yo6tPGaVJQxIDkYI5I4AAAggggAACfVaACrI+e2kYGAIIIIAAAsch0N4uVVRI5ZVSdZ1UVCQVFrrmyu53YwanSykDpdhIKSFBiomRaPFzHNh8FAEEEEAAAQQQ+ECgzVWKrckt073vbFJlQ4vOcuuMXTl7lGIj+P1kfkYQQAABBBBAAIG+JkBA1teuCONBAAEEEEDgeAQaG6Q33pCeXijlbJO275JKaqSgYCkuXGpxwVmV2yfMrXmR7n6LeeRI6bSp0tlzpenTpdCw4zk7n0UAAQQQQAABBAJWoLyuSS+uz9XLm/YpJixEn52erdNGpSksOChgTZg4AggggAACCCDQlwUIyPry1WFsCCCAAAIIdFWguVlavFj6wU+lpTs+qBQbP9SFXuNctViye+4CsgO3Jrf/nlxpyRYpv8QFZy3S58+Rfva/UoarMAs+aP8DP8tjBBBAAAEEEEAAgQ4BK9wvrW3Q7S+v1Q73i0lT0hP0w/OnKMqFZGwIIIAAAggggAACfVeAgKzvXhtGhgACCCCAQNcE1q6Vfnqf9JoLyIa4MGzKKCnThVxdXQC+ylWY7XBh2docqbJe+sal0g1fccFaStfOz14IIIAAAggggECACtQ3t+rNLfl6du1eNbW26YKJQ3Xe+HTFR1KVH6A/EkwbAQQQQAABBPxIgIDMjy4WQ0UAAQQQQOAjAlY19tkbpBoXbF18lpTq1hY71jY+VkW2cbu0YJUUHS+99zdphGvByIYAAggggAACCCDQqcB9b2/Wqy4gS4oK0y0fm6yRKXFuaVfXypoNAQQQQAABBBBAoM8LEJD1+UvEABFAAAEEEOhEoKlJeuQf0s/vd4FYlPSJk6X4uE52PIaXcvdLr7jgbZCrRnv4V9JJ047hIHwEAQQQQAABBBDovwJ7y2r1yJLtWpVXqjPdOmOfnzlcKTER/XfCzAwBBBBAAAEEEOiHAgRk/fCiMiUEEEAAgQAQeOEl6bLvuPBqjHTKFCkstHsnbW0XX1go1TVKb/xFmjCxe4/P0RBAAAEEEEAAAT8V2JBfrvve2aLcilpdOWuEPjU5UxGhrN/qp5eTYSOAAAIIIIBAAAsQkAXwxWfqCCCAAAJ+KrBtm/Tpa6U2177n4rPl+vj0zERKy6XHX5cmujXNnvyda9+Y2jPn4agIIIAAAggggMARBFpbW1VZWam6ujpFR0crNjZWISEhR/hU977d2tauBVv36553NisuIlRfmj1C544d0r0n4WgIIIAAAggggAACJ0yAgOyEUXMiBBBAAAEEukGguFg67RKp3lV2XeLCscgebuVjIdlTb0jXfFr62W3dMAEOgQACCCCAAAIIHJ1AQUGBfve732nnzp2qqanxwrGhQ4fqqquu0siRJ2a91JrGZj21crde2rRPqdHhum7eGE0akqignvpFpaMjYm8EEEAAAQQQQACBYxAgIDsGND6CAAIIIIBArwi0tUkPPCjddJd0+XnS0MEnZhgrNkuvujXJlj/pWjpOPzHn5CwIIIAAAgggEPAC7e3tWrdunb7zne94lWNxcXEKDg72HldXVys8PFw33nijLrjgAu9xT4EVVdfrDwu3acmeYk3PGKjvnzdR0eHd3N66pwbPcRFAAAEEEEAAAQQOKUBAdkga3kAAAQQQQKCPCRQVSedfITU1SxeeKfcryydmgLV10sP/lj42S/rj3VJ0zIk5L2dBAAEEEEAAgYAWsMqxG264QUXu30Bf+9rXNGfOHEVFRWnv3r166KGHtGTJEg0ZMkT33nuvrKKsJ7aNbr2xP7y/TTtLajR/bLq+MGu4klwFGRsCCCCAAAIIIICA/wuc2Ibd/u/FDBBAAAEEEOg9gedfltbukn7gQrIT2c4nOko6Y6L01lJpi1v/bPpJJ9TAfnv8wO0jz+1Nb5f/v589ancVd/UNDaqtq1edC/lq7L7e3dzaJXarcY8bG12rSrcFBwUr2JkOCA5y90EKCvrwLdg9H+ACSfutdd979po9HuBuvsdBbh97Ldp9eZeUmKjEhA9+0907idzxOzLNAx/b5ex444Nd+S8CCCCAAAII6KWXXvLaKn7729/WxRdf3CHS5v6O37p1q/d37i233KKMjAzl5eUpLS2t29Yls39LLN9VrLsXbFJTS5u+PHukPj0tq2MMPEAAAQQQQAABBBDwfwECMv+/hswAAQQQQCBQBJ59UZqSfWLDMZ/txDHSm6ulhcu7NSCzgMqCq3oLrCzAcvdeoGX3LsCqqalVTW2te71eTa5yrqm5yd3svlnN3vMW97q95m7ec/eeu2/29mvRwWGabzon6t6Cr8iIcEVFRrrfeI9UZGSUdx8dEfGf55GKduvIRbr3oiIiFeH2jXDvRf7nZs/t8/Y8JiZGMRZWsiGAAAIIIBAgAgsWLFB8fLwuucStv/qfzarHbr75ZrW0tMjCsWnTpunFF1/UPffco1tvvVWnnXaab9djvq+qb9Iza/fq3xvzFB8RqqtOH6VZ2cnHfDw+iAACCCCAAAIIINA3BQjI+uZ1YVQIIIAAAgh8WGD3bun1Na7F4twPv36inlmF01jXuuitBdKNX+nSWe2Lq/LyChWXlrlbqXdfUlquopISlXivlanBVXjZb4G3trZ5995j97ytrVVt9tpB1WO+E1vwFB4e5sIlFyy5W6QLmRITElzllj32vRbu1gdxzy188gIqFzx5+/4nlHKPbe0Sq91q9c7Z5qrO2t1jd+7/PG9zz9vaD3zdPXfv2X4tHfv95zW33wefa/eCvYrKSpVVVKiiqloV7r6sskqlzmHHrmrvSz3fXHz3NievOs1371Wnudf+U9EW7KrbYlxl2tCMIcrKSFemu890vzGf6R7HxcX6DsM9AggggAAC/UbAWiz61h3zTeruu+/W9u3bddVVV+njH/+49/LKlStV6f7eXb16tebNm3fcldn3v7NF77vqsSHxkfrhxyYrM4n20j5/7hFAAAEEEEAAgf4kQEDWn64mc0EAAQQQ6L8Cb78thbnF4NMSem+OIzKlJxeodPNWlYSGqNpVd1VX16jIhT6FhUUqLilTuQuBKqvsVu3er/ECI9+ALQAKDQ1VhAulvEopd5+clOQ9tqDKKqUsAIqPj1OCu8XFxnr3CXFxrnoqWuFh4V4oFuGCsTC3f5A7nj9uVtVmrR5ralzVnKuOsyq56toa99i95u6r3Wu13mu1rrKuwauQa3RVcnX11i6yTltytmvVuvUuXPygPaQZRLvKskEpyUpNtttApdpjd4uLdVVnzjTa+VnbR7tFuTDRgjg2BBBAAAEE+rpAomtXvGfPHq/NYmpqqiwcW7p0qS688EJde+21Xutjm8PUqVO9doz2Cyn296z9m+NoN/ulnB3F1XrovS3aVlyjkzKS9JW5ozUkMfpoD8X+CCCAAAIIIIAAAn4iQEDmJxeKYSKAAAIIBLjAphwp3AVksb34JU2ca+8XEaK/fPeHWpgQrwbXHrGxsakjBLN1uBLc6wMTEzR4tFvA3n2pNdBVdSUlJSrJ7t3r1iow1IVrFpR5txDf4w/u7Rj9fbMv7Xxh1SAdvl2TVaS1tLR6FWfWStKCMu/m3C1cK3DBpN32FxVrv7vfuXuPlq5c3dFaMsw5W6WdBZAWTHpVdy4kszAtO2uoxo4coZEjhslCSDYEEEAAAQT6moBViD3wwAP60Y9+pHHjxumFF17QmWeeqRtuuOFDa43Z35e2WaB2LOGYfXbxjiL9aXGOCqobdOm0bH3GrTcW69orsiGAAAIIIIAAAgj0XwECsv57bZkZAggggEB/EbA2gzsK5H5N2lWRhfXerFywpbAQDXaBzbjRIzV0SLqGDB6sIWmDlJ6e5oUuwTZGtm4TsEqvsDC7hXprlnXlwNbassAFZoWFLjQrKlK+C86K3POi4hLYrX0wAABAAElEQVRvXbfyikr3fpEWLV/pKtTq5X66vGs3euRwjR4xXGNGjXAhZ+IHIV60a0/pArVACC67Yss+CCCAAAI9K1BdXe3aLrd6LZHtlztOP/10Pf3009qyZYt3S0tL07e//W1vXU7fSGz90sWLF3u/eDNhwoSjDsga3b9r/r0hTw8tyvECsetd1dgnJ7uqeTYEEEAAAQQQQACBfi9AQNbvLzETRAABBBDwe4HmJqmw0gVkLhxz61D12uaqveRCssvmnybddFOvDYMTH14gxF2njPTB3u3APe236+vdmm91LhSzVo0VtiZaebn25O7T7j252rU3V4uWrfSq1WKio71qwETX6jIxPt4LQ4e5irPszKHeY2uRyYYAAggggEB3CdhaY08++aQ2bNjgtRZOSUnRJZdcounTp7t/ctykW265xfv7ydYS3bt3r+x9+yUSW3fs/vvv15IlSzR79mzNmTPnqIZUXtuofyzfqTe3FSg1JlzXzR2jOcNTj+oY7IwAAggggAACCCDgvwIEZP577Rg5AggggECgCLS6tkHWOii8l//aDnLrediaHk3NgSLfr+ZpXyT6WjumaGCnc6t365xtzdmhDe439TdsdmuwbN/tArQ8LV+9Vs3NzWpxv9VvAdyw7ExNGjfG3cZ6FWcWqIW76kardGN9s05peREBBBBA4BAChYWFuvnmm7Vp0yavcsx227lzpz73uc95f6ecffbZ3t9Bd955p3bt2qXrr7/eC8hsTbKcnBwvOJsyZYp+/vOfu3VNIw5xlo++nFtWq7ve3KDtJTUakxqnb509QUMSXDtpNgQQQAABBBBAAIGAEejlb9oCxpmJIoAAAgggcOwCrsWQ4t3aYzsa5BaX+iCkOvajHfsnm1sku7nWimz9UyAyMkJTJ0/wbjbDZteusaioRIUlrkWju7fWjXn7C5Tvbm+9+77+9eIrCnKh6cCBicocMkSZQzM0YliWRg0fpuFZmd56c/1TilkhgAACCHSHQE1NjVcdtmPHDn35y1/WzJkzvRaJ5a7Cefz48R2nmD9/vjIzM/XYY49p6dKlKikpUWlpqWvxnO61YbziiiuOKhzbWlCh376zRbtKa3TumMG64uQRSo7perjWMTAeIIAAAggggAACCPi1AAGZX18+Bo8AAgggEBACrvJHI127nxXrP6jeCu+ldcg6ArLBAcHOJK2jZojLQ9O8m8+j3YW0vlthSanWrd+oJStW6b3Fy7RizTrvi80BLjSz9ehOO2WWzpg7RzOnTXFVAXzx6DPkHgEEEEDgA4FnnnlGa9as8arFrrvuukOuH2brkmVkZOgnP/mJK6pvc+tsFnqtGIcOHepVmdnfO13dNu0v1/++uEb1za369OQMXXPqmEOet6vHZD8EEEAAAQQQQAAB/xQgIPPP68aoEUAAAQQCTWCEWyzeAqr6etdqsZcCsnq3FlqDu2UNCzR95nuAgH0J6fsiMn1Qquz2sXPOlH15uX3XbteWcae279yt3H35rk3jVi14b7EXlo0eMUwjh2Vr5PBsZblKs8GDBinZVZ7RkvEAXB4igAACASTQ2NjorR0W7dr0zps3r+PvloMJrFrM2ifadtddd3l/p1jl2LFsq/aW6r53t6ixpU0XT83SZTOHH/K8x3J8PoMAAggggAACCCDgXwIEZP51vRgtAggggECgCsw7Vaq7TyqulBLie0chr0Cami1luxsbAgcJWMXYmJEjvJv9dn+D++LT1jTLLyjUSldZtnTFaj338mvui80gRYRHuFZY4cp2Qdn0qZM1fcokjR098qAj8hQBBBBAoD8L1Ltf+rEWi7a2ZVTUodf+CnNrXG7YsEFVVVXa4tbIHDdu3DGxvL2tQPe+s9lVoLXrv+eO0Xnj0xXq/k5iQwABBBBAAAEEEAhcAQKywL32zBwBBBBAwJ8Eps+QRgyS9hVJo1w1WW9sm3ZLX/6MXMlPb5ydc/qRgFWFRUVGereBSYmaNH6svnTZZ1VSVq7NW3O8yrIt23JU5Fo0Pvrkv/TgI49qUGqKxruQbPzY0W7/cRqUkqxEFwb7qtX8aPoMFQEEEEDgMAJNTU1eFZgFXxEREaqtrdW2bds0ceLETj8VFxen//7v//aqyGz9saMNyFpa2/ROTqEefH+bQt3fT1eeMkLzJwzx1tDs9IS8iAACCCCAAAIIIBAwAgRkAXOpmSgCCCCAgN8LfOEC6d7HpTNcWHait/xCqda1dzxj7ok+M+frRwLJLiyb59Yls5tVDlRUVavUhWZbc3Zo/eYt2rBpi9eSMd59GWr7Thg3WidPn6ZpkyYqOvrQ1QX9iIipIIAAAv1awKrAfvvb32rWrFk677zzdNJJJ2n16tV6/PHHddZZZykhIaHT+WdmfvDLQdbO92i2xpZWPbZ8l55fn6u4iFB979yJGj0onnDsaBDZFwEEEEAAAQQQ6McCA9wi6+39eH5MDQEEEEAAgf4jsHuPC6gukcZkSydPPnHzcl8u6R+vSENSpWcfklLcPRsCPSDQ1NSsNes3auGSZVq1dr0qXYBW4yoL4mJjNNeFanNmzdRot4ZZrHse6aoO2BBAAAEE/EegvLxcl156qezeAq9//vOfsnXILr/8cuXl5bkOztn6zW9+I1tf7MDqYWvba6GahWgPPfSQJk2a1KVJ1ze16NHlO/XsulylxkR44djYtF5qU92lEbMTAggggAACCCCAwIkWCL7NbSf6pJwPAQQQQAABBI5BwNbnyM+TnnlHGp8lt5DTMRzkGD6yI19atkm64ya5X/k+hgPwEQS6JmDrmA1JT3NB2HSdOXeOZp40RZkZQ9x6MW1asmKVXl/wrt5futxb06zMVZ4NcK2yEuPjvXXNunYG9kIAAQQQ6C2BRYsW6eWXX/aCsJtuuklDhw5VeHi4JkyYoJUrV2rv3r1asWKFrKViVlaW6+gcJGvH+Mwzz+jhhx/WjBkzdMUVV3ivH2kOZbWNuv+dLXp1S75GJMfqO+dO8CrHjvQ53kcAAQQQQAABBBAILAEqyALrejNbBBBAAAF/F9iyWZp6oTRhhHT+HLce2ICenZG1Mnrs3+58rrXRC0/27Lk4OgKHEaiorNTCRUv1mgvJcnbuVnNzs1rcz+fQIek69/S5OmPeHKW5dcxCQkMV7L5UZUMAAQQQ6BsC9ksOtnZYYWGhfvWrX+nqq6/Wtdde+6HBWZvF733ve7IWjLZ/cnKypk6d6q1Nlpubq6SkJO+zU6ZM+dDnOnvS1NKmn7+6Tsv2lCg7KVq3nj9VafGRne3KawgggAACCCCAAAIBLkBAFuA/AEwfAQQQQMAPBf71rPS1n0gzx0tTxvbcBOobpBcXSo0uJHvit9KMk3ruXBwZgS4KWCiWt2+/C8l2ee0Yt27fod17cuX6cWmka784esQwt2bZBI0aPkwZLjxjQwABBBDoXQELx771rW8pLS3Na6V4zTXXfCQgsxGWlZXp0Ucf1ZtvvqmCggLZemPxrkrYKscsUBsxwv1y0BG2vWW1rnJss9bvr9CMzIG67tTRykiMPsKneBsBBBBAAAEEEEAgUAUIyAL1yjNvBBBAAAH/FXBfGOlHv5B+9WfpojOk0cO6fy62ROnry6Wl66XnXDh2wSe9AKL7T8QRETh2AasyaG5p0Z7cPL3xznt67a13VF5R6VouBis0JESnz52tz174SRec9cD/Ro592HwSAQQQCCiB+vp6ff/739fixYu9eVtLRVtrLCEh4SMO9ue6tVWsdFXDpaWlGjZsmEJdZXCI+zP9SFt9c4u+8/QK7Sqr0azMZH33vImKDjvy5450XN5HAAEEEEAAAQQQ6L8CBGT999oyMwQQQACB/izgvjTS1d+RFiyVzpzuWiCOlEKCu2fG1bXSu6ukvGLpF99w57mqe47LURDoYYG6unptzdmu1es3avW6DdrpKsvq3BezJ02eqNNPPUUTxo32WjKGuS9b2RBAAAEETpxAY2Oj7rvvPj355JNeZdjZZ5+tH/zgB16FWHeMIqeoSne8tl5FNY06Z+xgXesqxyJCu+nfRd0xQI6BAAIIIIAAAggg0CcFCMj65GVhUAgggAACCHRBoKZGuut+d3tEGupayX1ithQR0YUPHmaXimrXVvFdaX+ZW3vs19KnXOUY6zkdBoy3+qJAm6uArK2p1d59+d6aZQsWvq/aujrFxcZq/JhRumD+Oa5j6FSXKfPlaV+8fowJAQT8X6CkpEQrV67U/PnzOybT0NCgl156SXfeeacXks2bN0+33HKLEhMTO/Y5lgf5FXX6n+dXqaS2UZfPHK7PTs92S7QOOJZD8RkEEEAAAQQQQACBABMIvs1tATZnposAAggggED/EAgLk1wLOWXEuXaIrm3R8o1ScJAUGyXZe13d2lw7xbIq105xnWun+L40OFl62rVVPOcc2ip21ZD9+pTAAPfFaHh4mFKTB+qUmSdp/pmnKykxQYXFJdq5e49eev0tLVm+0oVm9YqJjna5cniX2nf1qUkyGAQQQKAPCzzwwANexZitKzZp0iT352yE9+fs+PHjNXjwYK1bt05btmxRTk6OJk+erLg492+Zo9zslyE2F1bqpqeWyf4pc8mULF02a7hbkpJw7Cgp2R0BBBBAAAEEEAhYAQKygL30TBwBBBBAoF8IWHXX1KnSeadKoe4LoT+/IO3cJ+1x7RGD3bdFie4Lp0N9UVRbJ23a7toprpEWu7XGItyxbvma9LPvSuPG9wseJoGACURGRmrC2DE6Y+4pmj3jJMXFxGjb9p16a+EiLVq6XGs3bpKte5OWmuqyZdov8lODAAIIHK9AYWGhVq9erQ0bXLvbnTt16qmnuj9fP/jlnREjRmjixIne+5s3b9aKFSs0evRoDRo06KhOu3hnsX75+npXDRyk6+eN0aemZh7V59kZAQQQQAABBBBAAAFaLPIzgAACCCCAQH8S2LFD+r/7pBeWSVWuKqyyQUpyFWVJ0VK8u7W2SeWuNWOpW2fMVc94AVpcrPTVT0nXfllKOL42R/2Jkrn0b4FaFxC/u2SpXnl9gXbs3qva2lqlpqZo/tmn66y5czTIPY483pal/ZuQ2SGAAAKHFXjllVf061//2v1zpErZ2dlea8WsrKyOz1hwZi0WrYps4MCB3vtWYRZ0hNbOra5cbNnuEt3+ylrFRYTq22dP0MwsV/3OhgACCCCAAAIIIIDAUQoQkB0lGLsjgAACCCDgFwLFroJs6zbXdnGTC8NKXQvFMheMVcj1N3JhmQvBEpOkrCGu+myM3K9tS67NHBsCgSjQ0NDoArI9Wr5qjdd2cXPODiUmxGv0iOGad8osnXryDPc/GYLjQPzZYM4IIHD8ArYO2R133OFVkY0aNUrf//73NWXKlI4D79mzR/fee6/effddjRkzRg899JDXjrFjh04eLNhWoN++vVkDo8N105njNSE9oZO9eAkBBBBAAAEEEEAAgSMLEJAd2Yg9EEAAAQQQQAABBAJEYPnqtXr86ee0ev1Gr+1itGvPeOXnL9EnzjtbUVGRfrO2TXNzs7Zt2yar4MjIyJBVZdgtODg4QK4k00QAgb4isH//fl177bWytou23XnnnTrttNM6/jxtaGjw1iv78pe/rMQj/ELCjuIqfePJZW7J1QG675KTlTUwpq9Mk3EggAACCCCAAAII+KEAAZkfXjSGjAACCCCAAAIIINBzAq2trcrZuVuLl6/U+0uWu8e7lDl0iE6dNVNnzjtFY0aO6LmTH+eRm5qa9Prrr+vPf/6zcnNzvZDPd8g5c+boS1/6kqZNm+Z7iXsEEEDguAX27t2rrVu36txzzz3ksUpdNfvtt9+uRYsWKSEhQddcc40+85nPuMJ2V9neha29vV3v7SjSL15br8FxkfruORM1Ni2+C59kFwQQQAABBBBAAAEEDi1AQHZoG95BAAEEEEAAAQQQCGCBtrY21bvKhrffX6K//fNpFRQWKTIyQnNnz9IXP3uRhmak9zkda0/2yCOPqLGx0WtXdvHFF3tr+yxcuFDPPfecq4KL0sMPP6xhw4b1ubEzIAQQ8D+B8vJyXXXVVSp2rZ0vu+wyXX/99R2VYQfPpqSkRF/4whdc1+cyLxi76KKL9M1vflNhYWEH7/qR59uLq/Xdfy1XY0ubfnXhdE0ckqgBH9mLFxBAAAEEEEAAAQQQODqB4NvcdnQfYW8EEEAAAQQQQAABBPq/wIABAxQWGurWIxumj511hgvEBqumpk6r123Qv994S3tz9ykqOkpxsbHefr0pYmHeU089pd/+9reyCrhBgwbprrvu0uzZs5WVlaV58+Z59++9957Wrl2r008/3YV9kb05ZM6NAAL9QCAoKEgWfOXk5Gj58uWywGzy5MkKDw//yOwsoE9OTtb777/vlj6N1ubNmzV27FhlZmZ+ZF/fC65wTCtzS/S9Z1cqKSpMt35siiZnJBGO+YC4RwABBBBAAAEEEDguAQKy4+LjwwgggAACCCCAAAKBIGAVDiNd1dWps2bo1JNnqLyiQq+/s1DvLV2ulWvWeZVl6WmDFNJLa3xt2bJFv/rVr2QtFuPj470vqSvcGM8444yOag77Etr227hxo2bMmKEhQ4YEwqVjjggg0IMC1iJx+vTp3p87FnzZ2ocbNmyQtXTtLIS3AP+1117Tj3/8Y6Wlpem88847bAXZlsJK/fSVdQpxa47ddNZ4nZSZ3IOz4dAIIIAAAggggAACgSZAQBZoV5z5IoAAAggggAACCByTgCso877IHZiUqNNPPUUzT5qq2to65ezYpVfefFur129UqKs4S4iLU9QJrs56+umnZdVhVrnxf//3f7I1gayt4po1azRz5kyvWiPYhXd79uzRkiVLvIBs9OjRx+TAhxBAAIEDBezPlvHjx3ttXVeuXKnt27dr9erVmjRpkpKSkjp2tUrXBx54QHl5eV5bRgvwD9decVtRlb719HJFhgbrhjPH6ZThqR3H4gECCCCAAAIIIIAAAt0hQEDWHYocAwEEEOgHAitWrPAqC3bt2qXObvn5+V5FQkREhOx28GaLp69bt06bNm3y1riJiYk5eJeAe26mthbQlClTOm01ZCCFhYVatmyZ92WRLVrfma0PLjc3V/bFU319vVJSUnwv94n75uZm2Xx37Njh/RZ5Z7813tWBWpumP/7xj94crRVTb2xWZWM/z/v27fN+w92+/GND4GCB1OSBmj3jJM2aMU3xLhTbsGmzXn3rHa1YvU41dfUalJLsgqmogz/WI8///ve/y/6MuPbaa71A7NRTT5VVkL355pte67OTTz7Z+7PZKjfs5/uss87SqFGjvLHYn0PW7sxaSrIhgAACRxKwoMtCeAvCLOCy1om2ZWdna9y4cVq1apV27tzptVy0f69Ym1f7d+Jbb73lrYE4fPhwXXDBBYf9N8+G/Ar96KXVCg8J1tdPH6N5I9OONCzeRwABBBBAAAEEEEDgqAUGuH+ouq7ebAgggAACgS5w5ZVXeuHWkRyslY59sXrjjTd+KKSxL0tuuukmLVq0SD/96U81f/78Ix3qkO/bX001NTXeFylW+ZCenn7IffvqG42Njbr66qs1cuRIr43Qocb5m9/8Ro8++qhsDY9bbrnF+8LoUPs+9thjuvvuuz1bM+5Lm30R///Yuw/wqKqtjeOLTiihhd47ShfpXQEB6SAqWFBR7A0UFQtiwe4VOwqKFVRAehEEAQVUFOlI76GF3iKBb9597+QbxkkBUiaZ/34eTDJzyt6/c2YS95q19h133OECflr3SOXbLrSp/NL1119vZT3l7IYOHepsLvRYF7Kfzn/ttde6TBsFDZSNU7t27Qs5VMB9FExcu3atC8D16tUr4DY8mDYFjnqyySZOnWGTZ862PXv3WXh4but69VXW5ep2lssTKEvOAJReM5qs/uabb9xrR4K6l/Uamjp1qhUqVMgGDRpkL7zwgh0+fNi9l9SqVcv03n3jjTda+/bt7brrrjOCwWnz3qPXCKSEwOnTp93vrhdffNEFyLxTCeXLl7ennnrKBcf094zWIbv33nvde5J+1nuNfp8qq1U/DxgwwLp37x5nl6OOn7KbRy2wTJ6yinc3rWxtLqUcbJxYPIEAAggggAACCCBwUQKZL2pvdkYAAQQQSHcCVatWtWrVqv1rXMeOHXPBD5XMUQaCyuO89NJLVrRo0dhtNemhydWLnQTes2ePCxZFRka6cmGxJ0gj32gCSUGiQ4cOWZ8+feLstYJos2bNcoHGvXv32qRJk+INkMV5oCB5Qtc+KSbXdYxHH33U/Rs3bpz16NEjRUe4cOFCFxwrUKCARUVFuaBvUgbIdM0VtLiYIHKKgnCyRAsoCNbrmq7WqkVTW7pilc3wlF0c+eU39uP8hda+dUtr0biRFYz4/3JjiT5wIjasXLmym4zetWtXbIBMr6UnnnjCvU+PHDnSHnnkEZeBqjKMyvRQ01pByvz8/vvvrW3btqb7noYAAgj4CyiY/tVXX7kMb/29p+x4fVVgXu8h+uBUu3bt3Idl8uXL5/4O+uijj2zKlCkuw1zHK1y4sPXt29cF5P2P7/15+c4D9uzUpZYja2br26iitb4k7X1IyjsWviKAAAIIIIAAAggEvwABsuC/RvQQAQQQSFGBBg0aWL9+/eI859y5c23w4MEu20yfBO7Zs6fbVpMkCgqpXWyATME4BeCUrZYW22+//WaTJ092E0ClSpWKcwgzZsxwQcfbbrvN5syZ49brUOmzKlWqxLlPsD6h8pDKhFO72OuvYyhQq0l8Zcwpi1DlmVKi6dPwY8aMcSWjlE3z7rvvmu75u+++O0nGpTEo8KsymbT0K1DIU1qxTctm1toTKJv/y2L7YNQX9u7Ho2z0uIn24J19rWnDekk+eGVoTJs2zb799luXweld10fvzcpmzZMnj7388stuQvvxxx83vWZ1v8+bN89lminAFu4pE0lDAAEEAgkoCKb1w3Lnzm3vv/++KWtMTX+zPfPMM+69RJnu+vCPMsSKFCniPuyksq/6AJCCY02aNDlnTTL/80SfPmPPTvnLjv8TY3c2Lk9wzB+InxFAAAEEEEAAAQSSXCBjkh+RAyKAAAIIpHkBBTji+tesWbPY8nk///zzOWPVRKz+xRcg0YRsdHT0OftdzA86njK2zqfpU9AqPRZXu5j+6bha80dl9Fq3bh1nRtXJkydt9OjRLhCjtYE6dOjgujN8+PB4+xZXn72Py0PnPt+WkEmg42msvo6Juf46jnwTMtYaZo0aNXLX9ocffnBl4AL1Iakf0/p7WjdFWTTNmzc3BQ02b97sgmSJPZcsNUGYFE3XUsc7n5ZU5z6fc7JtYAG9FzZr3MDefeUFu/OWGyy3Z23GwS+/YY8/97L9svh3z+vn/K5t4LP891GVvm3atKlb80eBXU1m79u3z32YYeXKla6c2bPPPmtPP/20af0ftePHj9uiRYvc961atbIsWbK47/kPAggg4Cug9wpldiuIrqwwlY/2/p2oNWcVfL///vvdYwqGKVivpm0UKFOQrFOnTvEGx9ZEHrLen/zk2eus3eHJHOtUM+4PGLmD8x8EEEAAAQQQQAABBJJAIG1+ND8JBs4hEEAAAQQuTEAlu0qUKOF2VglBb1Ng5vPPP7d169a5rLLq1at7n3JfVfbr008/tdWrV5vWqypevLjVq1fPbrjhhnMmZSdMmOCCEUeOHHETK8OGDbOIiAi3nW8WkYIWypT466+/3Hpl+iSzAlIqW+cfoFPpsCVLlth9993n1s5QqTEFdhSU6t27twvq7d+/3z755BNXBkgTQfqks0pNduvWzUqWLHnOWOL7Qf1WxpGyNS677LI4N121apXLktPEkf5pgumzzz6zpUuXugBNxYoV49w30BP6BLcynxRMUv81Ad65c2dTQPOtt95y5poc929aC0tlDFVmTfvpvCqR1LJlS/9N3dpGy5cvd46LFy+2L774wtl16dLFrr76auene0Lrankn4L0H0QS9SnMqQ27nzp3uGslY6ybpXP7XTPs1bNjQZbgou07baf2S5G4an4IKun91n+v+1LoqWodMlvGVkNyyZYu7hipDqqBtwYIFnUvHjh3dPa7XwIgRI9xrQOP4/fff3bE10ag1AL1NATEFT3/66SfTPipVpf6o1GSZ/5XF824rS32SX8/rOqjsqe51vR50v2sdN1rqC+TLm8eu7drJWjVvapOmz7JPv/7G/vxrudWsdqndcXMvK1+2zEV3UhPXCn7179/f3T9ad0xZuApG58iRw2V+qISib9P7sd6z9VpUQJqGAAIIBBLQB0eU2a8PjgRaF1bvNfo9rd9f77zzjsv+VsBev78S0/YePWkvzVxuJ07H2C31K1hngmOJYWMbBBBAAAEEEEAAgSQQIIMsCRA5BAIIIBBKAkePHo1dS8J3sl4BMk3MT58+3QVAvCZ6XOUGVUZw/PjxbuF2BTqUqfPBBx+4wICCQt62bds2U9BGAQZlz+h7BW8UAFJT5pUCXvo0so6nUnXZs2d322hy+Mknnzzn/NpH+6tfynh7/vnn3YSwJnuU6aXAjPqt4IOOp8CZglVbt251JQNVZk/91zgS08aOHevWHtPx4srG0LF+/fVXNxaVU1QAUGu5KTilcXozOhJzPm2jwMy9997rAiUK9Cn7SmuCyEMBGa15pfH7Nrl98803rnSgxqeso2zZsrkyj4899pgNGTLEtBacb1MwUsdREO7VV191gTydR8fX9VLJTT2ndbt8m66zrpcCceqfgofKNtPxtD7SCy+8EDCjTIFJlVnUBL4CiinRvNlqCrbq+im7T8EDBapUOjNQ0z2jdVl0r+hT85og9N5DClipnJ1eNzJXAE7HUpOTDPRa8DY9r6Dtf/7zH5e5psCGAsoKBqvMoz6Z75shePjwYXdNfvnlF/fpfT2voJkCmP7XwXsOvqaOgN5rIgrkt1t697QRw16zZo3q25r1G+zhJ4fYux99alu2bT8nI/NCeqkgmYK5N910k3tPUUBX5dD0gQS9T3izPHVs3acff/yxe29TMNVbktH3vHpf0GuWhgACoS2wY8cOB6D3iUAfaNGTCpLpwx76YIb+lvH92y4+vc37j1r/736zgyei7ZpaZaz7ZWXiPEd8x+E5BBBAAAEEEEAAAQQuRIAMsgtRYx8EEEAgRAWUYaTJV03oKwijkl4JNQWfXnnlFTcZq/I7+kSx9j1w4IDLtlFWkYJWWr9KARp9Allr6ShAo8ndQYMGmdbxKlSokDvVggUL7LXXXnPZRHpO22pSRsdTIEbHU2BDQQkdz7fpU83KbNI5FPxRlo0yvlTWUEEHBZlatGjhjqdMKAWDlBWnTCllM6nsXkJNWRtqysKKq2liWuNQoEyfxlZ/9U/rvykIo8whb2ZbXMfwPq4AiawUBFQGyD333OMCUArGKFinbDmdz79p/TOtGacsJwWpLrnkEuctFzkpaKbgzMMPP+wm1X33V9BN2XUKAiqgqSBWXE2T8vLTNVIgTOaaYFP/NHmmQNvEiRPdvRQog0WZacuWLTP1V2uRJWfb7MlK/OOPP9y91b59e3cqBbpq167t7gVlsqkP/llkusd17ylQ9sADD8Te48pEGzp0qFuXRda6J3Xvfvfddy6gpvHeeeed7vWgk+k6KdtPQUfdh1o3Kn/+/C54qaCp1n6Roa7LlVdeeQ6F+q1MIQVGdG00FlnTglOgQrky9vA9d9j2nZH2+ehvbezkafbTwsVuzbLuHdtb/nx5L7jjumfvuusu9z6n9wfdr1pvTIEy36bsMWU76oMOut/8m0o0qpSaAq5aX4j7yV+InxEIHQFvBvfff//tftfob5a4mn5n6u9EfbgjobYt6pgNnbHMDniCY73rlrPrLifrOSEznkcAAQQQQAABBBBIWgECZEnrydEQQACBNC+gDBgFJHybJv4VMNKku77XRIkCQCqRGF9T1oyCKcr60sS9gj7eTx4r4KXAWGRkpDvfG2941uXxBBAUhFL5Hk3qKqii770lDhVAUCaNshr69etnCmJ4j6csH2WP9ezZ0xTIUDaPsrN8m7KclB1VqVKl2IcVwFEfNKmsxeOVyaWmwFHXrl1d5pICWQrAJRQg00SyMs+U0ab942oqM6jMOGVSKWDobcpaUqBF/sqa0vpXCTUFZ3QsGQ0YMCDWSvspIKnMKx1PmSPepuuibDl5KrPPN9giRwXc5DhlyhR3zbwm3v01voEDB5pvycu4JsJU9lEBUWWQ+Y5Vx1JpyXnz5rmSlJqsDxQgU/lBtbmespW6P/yDU+7JJPiPrrFKXCpQqnVSvMEEb+BSwVKVRNTrQEErb9PrQVliusf79u3rvLzP6R4YPHiwC4IpwCV3XScFK9R0z/kaKptHGWV6TIEw34welXvUPajXkzL/9Nrz9lHHUsBRAUtlAtHShkD2bNmsQtnSNvix/jZ/0a/29Xff2zfjJ9kPc+db3xuut/qX17Y84ecGtRI7Mr3edZ/63qu+++q1r/d6b5Dee6/pftZ7mIK4+qfnVSJN76nKCvF9H/E9Ht8jgEDaFNDvPH0oQ9nTKoWs33kqe6y/h/T3gLfpgzD6naW/mfQ7SH/Ped83vNt4v3rXwYzv7yBtu/fISRsydant8nxtXbkowTEvIF8RQAABBBBAAAEEUlSAAFmKcnMyBBBAIPgFFOTRP/+mQJSCFfpkcOPGjd0EvQJY8TUFTXQsZbwo28wbzPLuo5+VQaNAjiZnHnzwwdhsGu82vl+VDaEAgs6r9Zb8j6dShQrcafJGgRf/AJkCD77BMR1bwT5N+uzdu9dGjRrlAmvKMtNEsIJzyvhRQCgxE8PKplLThHJ82ytDS+3aa689Z7wKRGliSllwChgqWOY/Rrejz3/kocmoOnXquDJqPk+5iS5lNCkI5tuU2aQgnRzbtGnj+5T7XpNirVq1cpldKkOpdbh8mzJJfAM7vs/5f69JNAUyA62FpbEpeKbmnVDz31/n0XYKTClApGucHE1BUpWIVH/812lS4E73gNZfUaDLm12mfijLRtddwVMFZf2bAlvKItOko14H8TWVR1TT68B/4lH3k9bXUxBPwVN5+AbItJ//ddZjtOAX0P3drGF9q3FpFft50e82YfpMe/WdD6xyBc86gu3aWKsWTRN8HzjfUar8prIOdb8rcK0+6F5WQFvlOpVJqvdFrcGo9wK998f3nna+52d7BBBIfQFVBXj77bfd31/6neJtymTXB2+UXe5tKt+qDxgN9nzoQ3+bKHiuvw38f1fpONpfwfn41mHdcVCZY8tt5+ET1tNTUvHmBhW8p+IrAggggAACCCCAAAIpKhD/zGaKdoWTIYAAAggEg4CCNjfeeKPrikq2KbNHJQi9mVG9evVygSNNqCbUtAaFgmQqdRhoUXftr6CQJl40UaNJ2fgCLyrtowwHBQY0qROoeYNUCiL4N/+AmZ5XYEMBP61JNmHCBPdP6/UoCKLgkbKcEjsx7J1gUoAsLh9NQisTSZNKCrx416Py9lVBEAXINHmtCapAffZuq6+6LmreoJ77wec/KrenrCtNZnmbPi2uDBJNgKukX6DmPa4Caf7Nm9Xl/3ign2Wpa68AmCbllYWoYyqzTcEmr5n6E6hpAl/XW8FR3UvxBchULk5j82+6xi08JeTiu47KxFOGl+4/3a/+10VBBAVxlcGlAJr3WN77TRmR3mCf7/l1nRW8TKip1Ofu3bvdubUWXaCm7DMFNbWtPHybjOKz8d2W74NTIK8no/Tqq660K5o3tu8mTrEvvxlvL775tk2d9aPddctNVqGcMrgSft9NzOj0PqT3PE1gq4St3k9Hjx7tSqcpkKvSpgrUKsvVt+l9JK73Nt/t+B4BBIJbQH+fqXSqSjrrd54+xKRS0soQ0weM/D8Yo9Eoy10fylH5ZgXJ9HeWssn1u0/vC/odpvcNrVuoCgFxfSjk9Jmz9skv62yjZ+2xxuUKERwL7luF3iGAAAIIIIAAAulegABZur/EDBABBBA4PwEFTXzL6mhSXmtjqeyeJk1Uxk9riunxhJoCDgp8afJExw3UNOGv5zVZ4w2WBNpOj2lNCzUdU2tSxdU0KaOMMP+mbB7/pkkdZbFpkthbXlKL0WvtHWWUKeNMQUFldqmv8TWNQU3BCm8AxXd7TS6rVJmago99+vRx38f1H2Vv6fyBjuXdRxZqCjIGatrXG2DyPu8NfiXG0T9QpGP4l1z0Hjeur8pKka2CVxq3zBVE1NiUzea9roH217YamwJCup/iawpgqQSnf6tevbrLkonLUUHXhQsXmkpwKnCgcqBxNdn9/PPPseUivX2Ssfp6oU0Timp6ncR1nynQqU/l67rpulx66aWxp4urlF7sBnyTZgTCPNmKN/bsbk3q1bWZc+fZlJmzbeCzL1q3Du3sqiuaW6GCF59FqWCY7nsFj1UaVJPiCrBqAlwBYJV39ZYzVfBaH5RQqVYFqXWv6Xm9rrzbpBlcOooAAk5A677qd5k+wKLyxfo7z9tUEllZ0/5Nv0P195J+36lM86xZs9yHebwZpgqYKdNbf9v4l1T2Hiv69Bl756fV9svmfdasfGF7uFVV71N8RQABBBBAAAEEEEAgVQTin+lLlS5xUgQQQACBYBNQgEKBMW+GzqBBg2zkyJFxTuR7+69AlTJoNMGqNZI0qeLfFJTQ+k1qgSZkfLfXZK6aJmaHDRvm+9S/vg8UDIlrMlfbdujQwZWwU6aTPlGtbLKlS5faihUr7LnnnnOBC326Or7mXcRea3r4Zmx591EATaWHFEipWbOmlSlTxvvUOV81yaS1QBYvXuyy+eIKfmknr4k3wHLOgTw/aBLcP9vIG0xRuczXX3/df5dzfj4fx3N2/N8PWldLwUbdA8qY03pnOq/WJpHDq6++Gm+ATIeRp1pcQVb3pOc/mrRXOTj/pmBufMErBe1UVk5N/Qt0n+o5ZfUpQDZ58mT3SXsFsrzriSloFeiaa7/ENO859TrRNQvUZKjJRzXv9t7tAl0n73N8TZsCZcuUsttv6mUd2rSyFzyZZCO/GG0zfvzJHn/oXqtapdIFD0oBaU2Mq+mDBrp3VErxjjvu+Fd5WL03q8ya3ou872vaXhknyja+++67431tXXAn2REBBJJNQOWC9Tecfqfo7xv9TeXb/DO/9PeF/lbTh6f094iCZAqm671B7ye//vqr210Zp/qbQr/rA/1OUiL7179vtFlrd1nN4vns3hZVLGum/18f1bcPfI8AAggggAACCCCAQEoJECBLKWnOgwACCKRxAZUaVMkcrUGhbAKtzfXwww//a/0J32EqM0yTKZpkVekd/0l9bauyeyoZpzWavIEb32P4fu/NmFHpQTUd37+p5J0y0RSA8c2E89/O+7OCVgp6aKKoatWqrh8qL6Z/OpYy5xQoU3AwoQCZsqLUlFUUKFiizDSVF9TEkQKOCugEajpfv379XPkibV+vXr1Am7nHvFlxynxSYMU/CKhySBqb72SVt4SfgnDeyW7/EyijRNdFpQP170Karq0CZApOPfnkk9a5c+dzDqMJd51DTX0M1OTo3Sah+0PH9z9HoGP6P6YMN10zBSx1j8eVwaWyUgro6f7XtVQ5RpWQVFMmnCYQdR/7Nt37X331lQtSqm+B1mLT9rp3FGBVP3TsQOZaJ03nUNCZcoq+yun3e70+ixcrYi8+OdAmz5hlk6b/YI8+87y1a9XSunoyyooXLXLeg1dQXYFk3cd169a17t27B3wv0nvAyy+/7IL6CgQ3adLErUWmIK4mxL/44gu3Lp9e294PB5x3Z9gBAQRSXOD99993fy/ceuutVqNGjYDnV5ayAujK/lY5VjX9TaWM+muuuca9FygTVb+v9F6hv/X0N42C53G1Bet32/i/tlqpfDntrqaVLVe2c39fxrUfjyOAAAIIIIAAAgggkJwCfGQrOXU5NgIIIJDOBFSCp2vXrm5UWidL2VXxNU2qqhyfsmu8GTr+22uSVRk8WivLmw2lSWEFVRQc8Q00KWiggJAmaJXF498UINInmrUGhkr/JKYpsNS/f38XGPGWy/Pup8wj7yLzCoAk1LxBC5UhCxTwmThxorPQJFJ863jJQoEUBUM++eSTeE+r/imzSgE873pY3h0UgAqUaadrookuXZcff/zRu3nsV51X64o89NBDsZkmsU+exzfewKPuA32i3L/JdPny5e7hQF56QsExXW9NunmztfyPczE/69ifffaZO4TWXIkrOKYNZK0gr4K9WkdOTfeIgrFaP89bPtM98b//6F4YN26cffPNN7H3hDeI6Zsppnten8hXe++998657/WYXgdz5851WTwKzCWHhc5DC06BvHnCrVePLvbiUwOt+qVVbOykaTZw8Av247z/ZoKdT681kf3UU0/Zhx9+6F7ngQL1eu/Q+kS6z3VvXn/99S7I3bt3b1eSUWV2tXaj7smpU6eez+nZFgEEUlFAv/dVLlVZYnGVQdT6oPfff78pkOYNjqnL+t33zjvvuOwz/az3Bv0OVPBcgbb4gmOLN+21N+essvw5stmQDrWsVP7AZbd1XBoCCCCAAAIIIIAAAikpQIAsJbU5FwIIIJDGBRS0UikuTYgocKFF2hVgiKtpEr9Hjx5ucv/rr792AQSV6tE++nSySu8pqKAJ23vuuSe2VJcyZBRE0DkUhNu8ebML5ih4oU8u6zkF1rQmmkrOKUtHa44NHz7clF2m/bVmWGKaghsKeugT0Mok0nF0PGWWKcNC59B54/qUte85FLhQ8EnH0ESSb1OgZNKkSW6M+tS2N0jiu433e51PGWTy1gT1qlWrvE/962utWrVc4Eaezz77rCsDqJJH2kfZHyrX6N90XTp27OgeVpklZYOofxq3spQUVFPgUBl6gUoW+h8vrp8VhFNGlY6tkm4KQCoQpmumCTqVdPMGHnXuQG3+/Pnu/rniiivOyYILtO2FPKbAre4zBWdbtGgR7yGUYabgpcbw3XffuWCVJgSVQaNxvvHGG26cGq/ucQUIZangmbIPvdlj3sxHXSdlCKrclZoyMpWRpgxCTULqk/k6jjLxFixYYHoN6dyyUCkrWmgJaDK6fNky9oInm+yuW2/y3H+n7bnX3rKXh71nW7fvOC8M3YP6wEGg9yEFyJX5qQxTZW3q/VHvjVqnzxvU1XuIPoign73lGs+rA2yMAAKpIqDfId5/+nCSb9PvY/1N8Mgjj7i/u5TZrFKq+t2jNcv0958+rDFixIgESyP7Hnfz/qM2YuE681RYdJljhXL/O/vfd3u+RwABBBBAAAEEEEAgJQUosZiS2pwLAQQQSAcCCo4psKEMBE3kK8h13333xTkyZcXs3LnTZekokKAgg8pxKUiiAICyn5SpdMkll8QeQ5OyWjhewRqtZ6HghSZsFGTwlj5UVs7TTz/tgnWa7FWQQwEJNQXbElNeUdtqu9tuu80FOTQxNHv2bNcnTSDp/ApuKCintakSaprA7tKli/vUtdYx01i9bfr06S7YoQw4ZV4k1PTJbllrTN9//71VqVIl4C4K0KjspYKMyuTQpLWCJwqsKMAmizfffPOcT3arnxqTxqd9lC2mc2kNOJWn1Dl13AceeCDBdb8Cdup/D8pWgb63337bXUdl0Ol669rr3ApktmvXzmSjoKLWf/P/BLo3w02uSd2UJaMAnJqyw/zXXfE/nzz79OnjglUKICow0MITVFMJTGXYKBNNrwtdY1nq3tE9fvnll7tsHLmrlSpVyr0GFMSUsbbX60j7KEimMo8qy6h7SBOUCh4qgKvJTJ3nuuuuCxjY8O8vP6dPAQW1enRqbw0ur22fjxlrM2f/ZEuXr7RrOnewq1tf8a/X0PkqKLivDFy9PvX6VeBfwXe9f2vtIe+HFHTfKsi2fv368z0F2yOAQCoJ6PdcuXLlTFlio0aNch/c0O9l/T779NNP3YdrFART+dV7773XKlWqFJtZrdLQ+p2mNQkVMPd+6CO+oew9ctKGzlxu2w8et8fbVLfLS0fEtznPIYAAAggggAACCCCQ4gKZBntaip+VEyKAAAIIBJ2AJksUFFKgQFkycTUFCVQeUJP/3uyWmjVrupJvKvOnx5o1a+aCADqGJnNr165tWj9MgRdl1yhA4p2AUUDBf20vZVCpZI+CAgqaqOkYmtRREEHPaR0tPa9j6Zjqu7K8FFDTxI43GKF9lQmhLJz69ev/a60djad8+fKuz8rk0bi8GUCa/L399ttNGV/+gRsdN1BTBpnWqtIksz5trX4oEDNhwgQ3ljZt2sS7ppj3mNpPQT99olsT1QrCqG+bPdl0Cpap3KW3KeAoQ5mon7LQWBW4lIk++a2go8qjeZuCii08wR1Ncnuvi8at66KAjtba0nWVj7cp6+vw4cOuXKLMfJuCN8p2U1+bN2/uAo/aV8dQoEzZUt5rpXtCmWnKcFNgTsfVfgoc+gaptL1KuSlY2rdv30RfA99+xfe9fBWcU386deoUZxDS9xhai8+7dpvuTd3r2l/XR6XqfMep+1jrjimA6bv+nsaj+1jZlLpWGrMCsLp2Cka0b9/eBZV17WWg8yizTJOTCo753ou6Hr/99pt7vm3btr5d5ft0LKDXVh5PULxJg7qeIFVBW7J0mc2d/4tt2LzFKpYvZ7ly5jjnPfB8KHQPjx8/3r1nqMyaXuu6txVM1j99YEG/A/S+pqxd3du6L2kIIBD8AnrvKOP58M4PP/zgyieq/K/WElPZan1QRcEyZY0NGjTIZcT7/i2lffX7Wtn1Cpzp74z42qET0fbU5D9thyc4dlvDSnbVpcU9f1PEtwfPIYAAAggggAACCCCQ8gIZPBNhqnZAQwABBBBAIEUENKGvf5qEUdaT7+RLoA5ovQw1BXQ0OePb9CtMx1LpPh1PE7UJHc93f//vVS5Mx9I/BZ2UieV/Tv99/H/WpLEmllQ6UiUo69Sp479Jkv6sCS0F/xR08Q3CeE+ioKUy/qpWreo+He593PerHBUck7WOIcuLcfQ9tvd7BU6VnabjKlin4FFCTZP0Q4cOdVlTmqhXICrYmwJeGqfGq+wv3bdxNd1vMtc2gTx0DAXIVL5Rxwq0TVzH5vHQEtiybbtbl2zG7LkucNapXWvr3P4qy50r53lD6MMSyupVwP1TT0aJ971A5WtfeuklV8ZWmYwK1CrzUeVsFcj2NgV+9T6oYDINAQSCU0BZot5Svuqhfr/otdy9e3f3gSTv69639/qdpWx+BcofffRRl03q+7z/92971hybsWaXda1Z0nrXLW/ZswT/73D/MfAzAggggAACCCCAQPoXSHh2Kv0bMEIEEEAAgRQUUFBM/xLbfDOK/PdR8EpBrKRaj0kBmIs9noIZV111lZtA0ie0lfkWaKLJfywX+vNff/3lSk0qw0OlDBXc8jZlHmldLDVlOMXV5KgAjP4lV1MQKL5gkf951XetD6ZJO5VhTAvBMY1B1zqxlhpToKCm1+J8zbz78TX0BEqXLGEP3327NfSUXXzz/RH28edf2+yfFtgTD99rFcqVPa9Av+5fvQ9qHUaV9/S+B+s95oUXXnBlQVVOVE33sG8WiSbQlX26ZMkSF1xT5iMNAQSCT0BZ3MpcVqlsfRhD65l6X+tx9VYZ7CqtqL8ZfF/3/tvrQzdf/rbRpq/eaVdVKWq3Narkvwk/I4AAAggggAACCCAQNAKUWAyaS0FHEEAAAQTSi4AyJzTptG7dOleCL74gyMWOWRPSc+fOdWt6nT592k1wKQNOZRO1jpXWFVFJtLvuussiItLO2h/KVvnkk0+sQ4cOrvzh+WbyXawr+yOQFgVKFi9mTRvWs5iYM7ZizVr70VN2Ue8HZUqV9ASosydqSApKa40hlZxVFlkZTzk2b9N7mdZH3LZtm3tepV5VhlbZZMqcVNnUGTNmuBK7KtebN29eMh+9eHxFIMgE9HtVZaFV6lgf7omvqaS1sseU1dyzZ09XCjiu7aes2G5f/L7RLiuR3+5qVoXMsbigeBwBBBBAAAEEEEAgKAQosRgUl4FOIIAAAgikNwGVLNQ6HgrwaC2w5Gr6pPbMmTPtmWeeceuHaW0rTXqp1J+ysJTRpKyPFp71xpIzky2px9evXz+XvfLxxx8nOHGX1OfmeAikdQEFy7Ue2dA33zWVX6xV/VJ7YdBATwA97pKfvmPWGkNae1EBLr1/KNiltmPHDhdwV2aZ1h9T+VZto/eh//znP24tI733KGCmdQUVUNPahyrbllayQH0d+B4BBMyUOaaSilqfUOWala0e1wd/lm6LsmemLrVcWTPbW9fUs4hciQvM44wAAggggAACCCCAQGoJECBLLXnOiwACCCCQ7gWmTZtmP/74ow0ePNitaZacA1ZGx5QpU1xmhyavNUFdqVIlV+5RnxBPS23jxo1ubRQFGOMr45SWxkRfEUgNgZ2Ru23EZ1/Zwt//cIGtvjdcaw3rXW5ZE8gWUV+VCTZs2DDTOoclSpRwAXa9z3Tu3NmtUeYtmapAvLJVP/zwQxeYr1mzpptE1/qQWgNx586d1q1bNxswYABBstS4CTgnAh4BrQuokswrVqzwZJjGmF6nNWrUcIFsL5DWxHzuuedMAfYrrrjC/R2hTPhvvvnGrfdaunRp9/dMtWrVvLvEfvXEyO2v7VH2xKQ/LCJnNnu6XU2rUCjx5bRjD8Q3CCCAAAIIIIAAAgiksAABshQG53QIIIAAAqEjoEkoTUopkyKlsreUvaHJLZVJS6lzJvUVVf/1LyXdknoMHA+BYBFQAGvuz4vsjXeHuwBVl6uvsjtu7p1g9/Resn79elMW56JFi9w6RTlz5rSOHTva3Xff7dYU1Hvc559/bh988IHLXL3ttttc+TVllyirTBlnKu+qsmz9+/d3gbIET8wGCCCQpAL6faqSxV9++aVbW1AHr1u3rr3yyivnrFuq1/lDDz3kfv8q41PZ6NpXX/VhlSeeeMKVZAzUuf3HTtngyX/ahv1H7em2Nax+2YJuv0Db8hgCCCCAAAIIIIAAAsEkQIAsmK4GfUEAAQQQQAABBBBAIBkEli5fae+NGGXrN22xRvXq2G03XG9lS5dM1JmOHTtmyi7JkydPbMaJAmizZs1yJRiVtargmP75l1JcsmSJC5Ip4D1nzhxKpiZKnI0QSBoBfUjnnXfecVlgZcuWdYGxyMhIt7Zn48aN/3USZZkpI1TrmCrIrRKqyibr1KmTqYRzoHbweLQNnfGXbdx/zO5qWtmuqJy2stYDjYnHEEAAAQQQQAABBEJHgABZ6FxrRooAAggggAACCCAQwgJ79u6zL78dbxOnzbTixYraHX16W7OG9c9bRBPnU6dOtVdffdUFzm6++Wa3Zpm37KLvAXfv3u3WYtRj33//vRUvXtz3aTt69KgtWLDALrnkEitVqhRZJ+fo8AMCFyeg19zQoUPdOqSPP/547JqB3qMuX77cVEYxX758poCZyjMr+H3o0CEXIMuVK1dsUNy7j+/Xwyf/sbfnrrJft+y3PvXLW9dapX2f5nsEEEAAAQQQQAABBIJeIHPQ95AOIoAAAggggAACCCCAwEULFCoYYQ/dfbuVLlncRo3+zoa8/Kb16d3Tundsb2FxZIcEOqnWFnvrrbdc2cWuXbvaPffcE2gzt9aR1i9SK1iwoBUpUsR9Hx0d7b5qIv7NN9+0iRMnuon7999/3ypUqOCe4z8IIHBxAkeOHHFrkyrIpexOZYN525YtW+z111935VP1mMoodunSxQYOHOiyQBUwS6j9E3PGvvx1gy3ctM9aVSpqHaonLiM1oePyPAIIIIAAAggggAACKSlAgCwltTkXAggggAACCCCAAAKpLNC5/VVWsVxZ+3DUl/bpV9/ashWr7a7bbrKypRKe4FapxZdeesmtK6bSa/369Qs4Gq1PNn78eLfukbJS5N/vWwAAQABJREFUHnnkETfxruyzYcOGmYJkWtNs+vTpLmvl0UcftfLlywc8Fg8igMD5Cxw+fNj279/vXl/FihVzB1Bm2NixY91rU6UWlfWp193atWtt2rRp1rx5c5dJltDZTp85a+P/3GKTV2y3RmULWd8mFS1LpowJ7cbzCCCAAAIIIIAAAggEnUCmwZ4WdL2iQwgggAACCCCAAAIIIJAsAhkzZrTChQralc2b2PYdO23Bol9t0e9/WpVK5a2wJ8ssvpYlSxbLnz+/nTp1ypVYzJEjR8DNlWX23HPPmdZA6t27t/Xs2dNlqShT5eeff7Zx48bZsmXLXDm3F154wVq1akV5xYCSPIjAhQlobcCZM2favn37rHLlyqafVWZRQWmVNlW25scff+xen3peZVM3b95s3bp1S/CEq3cdsg8W/G0RubLbgFZVLX/ObAnuwwYIIIAAAggggAACCASjABlkwXhV6BMCCCCAAAIIIIAAAskskC1rVhtw751Wu0Y1+3zMWHts8FDr0am9XdOlo+XKGTjwpS61aNHCZZoo2OXflCG2cOFC02fwTpw4Ye3btzetUea7bcmSJd26RgqylS1b1qpWrep/GH5GAIGLFFAgu0mTJjZq1ChThqa3RURE2DXXXOOC1iq/qFarVi1TsFsB7YTaroPH7b15azwZYxnswZaXWOHwsIR24XkEEEAAAQQQQAABBIJWgDoIQXtp6BgCCCCAAAIIIIAAAskrkCNHmHVs29oGD3zY8ucNt8+/GWcvvvG27Y86EOeJM2XKZJkzB/6c3cqVK00ZYQcOHLC2bdu6jJXcuXPHHmvSpEn2zjvvuAy0unXr2vbt2+3uu++2vXv3xm7DNwggcPECeo3efvvt1r9/f5f1qTXIOnfubCNGjLA+ffqYNzimM+l1q/KpjRs3jvfEh05E28szl9vOQ8ftlgYVrGqxhNcqi/eAPIkAAggggAACCCCAQCoLZPB8yvNsKveB0yOAAAIIIIAAAggggEAqC+zbH2Wjvv7WZs6ZZ4U8pRbv6XuzNbj8skT3atOmTfbYY4/Zxo0brV69evbMM89YoUKFYvefM2eODRkyxK0/1r17d7vvvvvsu+++cxPzt9xyi6n0Y6Cm/13Rekp58uQJ9DSPIRDyAmfOnHFBruXLl5teL9WrV7dq1arFvqa0JqAC2/5N265bt869FvX8l19+afnyBQ56adsvFm+wbzxrj11Zuajd2bSyZc/y72P6n4OfEUAAAQQQQAABBBAIZgECZMF8degbAggggAACCCCAAAIpKKCyh9NmzbV3Pv7EcubIaf369Lb2ra9IsAcKiik4piBZzZo17aWXXjKVcvM2ZagMGDDArYeksosq+ZYzZ07TxL3KL8YVHFNgTCXiFi9ebE8++aRVqVLFe0i+IoCAR0BlEV9//XWbNWuWHTp0yL2eFEzWWmK33nqrZct27vpgq1atcmuTKYit1+vYsWNt165dLtNMgWvfcqi+wPPWRdqwuautfEQuG9SuloVnz+L7NN8jgAACCCCAAAIIIJAmBQiQpcnLRqcRQAABBBBAAAEEEEg+gV8W/27Dho+0Pfv22z233Wyd2rW2LFninhDfvHmzDRo0yHXo1VdftWLFirnvlXWiCXmVUTx58qQ1aNDAXnnllX9N2vuORPtERka6Cf+RI0e6DDNlyFSoUMGGDx9uviUbfffjewRCTUDBsffff98+//xzF5C+5JJLXBB6w4YNLvh87bXXujKL3nKKeg3269fPvSZlpWBYeHi4KYOzV69eAYNjKjezascBe2TCEivqWW/s+Y6XWdE8rDsWavca40UAAQQQQAABBNKrQODFA9LraBkXAggggAACCCCAAAIIJCjQoO5lnpKG4fbGu8Pto8++si3bttstva+1fHkDlzksU6aMvfHGG3bkyBErWrRo7PH37NljL774ogtyNWrUyAYPHhxvcGzHjh327bff2oIFC2zbtm2WI0cOu/LKK+2HH34IOHkfeyK+QSAEBX755RcbN26cVapUKTbD8tixY/bBBx/YmDFj7Ouvv7atW7e67M3ixYs7odtuu83mz59vBw8edIHsli1bWq1ateLUO3LyH/vs1w2W0RNMu/7yslYkPHuc2/IEAggggAACCCCAAAJpTYAMsrR2xegvAggggAACCCCAAAIpJBDtyVDp/+RztnzlKqtdvZoNGnC/RRTIn6izR0dH280332zr16+3woULu1KJBQoU+Ne+yhhTaTgFxpQJc+LECZetpjWUnn322djHlQ3z8MMPx1mO8V8H5gEE0rGAsse6du3q1vTTWn7KBFM7ffq0PfTQQ/bbb7+5dcf0OlRp0rffftut4xdXCcVAVKfPnLV35qyyWX9HWo9apaxPw4qBNuMxBBBAAAEEEEAAAQTSrEDglbDT7HDoOAIIIIAAAggggAACCCSVQFZPWcVB/e+1Jo3q2x/LV9iTL75qq9auS9Thld2i4Fi+fPns3XffNf/gmAJjKgX33nvvubWSVD5RpRSbNWtmQ4YMsbfeesvCwsJs0aJFLiimDLS41ipLVIfYCIF0JLBw4ULbvXu3aU0/b3BMw9N6fStWrLCmTZvac8895153a9assZtuuslmz57tSi8mhiE65oyN+X2j/bB2lzUqG2G965VPzG5sgwACCCCAAAIIIIBAmhKgxGKaulx0FgEEEEAAAQQQQACBlBUoUqiQPTvwYRv2wQibMP0He8oTJBv69GNWqXy5eDuiAJiaMlp8J/D12PHjx93aSRMmTHBrk2nb0qVLu3XMqlevbpkz//d/U5YsWeKCbFpDqW7dutqVhgACHgGVIFXLmjWr++r9jwLNen3dc889ptKnefPmdd/v2rXLlWHU62vAgAFWuXJl7y4Bv/4decgmLd9ueXNktRvqVbAsmfhsbUAoHkQAAQQQQAABBBBI0wL8lZumLx+dRwABBBBAAAEEEEAg+QUyZcpkd/W92e665UZPQOuUPfbsUPt50W8W48n4iqsp46tVq1Zusv7uu+92X//++2+3PlL37t1t9OjRscGxSy+91D766COrXbt2bHAsJibGRo4caQqead2kLJ5sNhoCCPxXQAFlNWVqqin78sMPPzRli+n1ouCYWsWKFa18+fJuvbFCnmC3gs2+6wS6jfz+s+PgMXtv/hpPxqbZI1dWtVL5c/ptwY8IIIAAAggggAACCKQPAdYgSx/XkVEggAACCCCAAAIIIJDsAkoK+3XJn/b8G8Ms+lS03dL7WruuW6c4z3vs2DG39liFChXs119/tfnz59uBAwdc0KtkyZJ2xx13uCDY1q1bTWuOvfbaay7jRQf8448/7M4777TcuXPb1KlTLVu2bHGehycQCDUBrUGmLLErr7zStD6fAmMKREdERNiwYcOsSJEijkSvwfvvv98aNmxoHTp0cAEyBcniageOnbJnpi61rVHH7NaGFaxTjVJxbcrjCCCAAAIIIIAAAgikeQEyyNL8JWQACCCAAAIIIIAAAgikjECGDGb1L69tjz1wt2dtsbz2/sjP7OPPvrLDR44E7EDOnDndpH1kZKT9+OOPdvDgQatUqZL179/fxowZY23btrURI0a48onLli2zJ554wnbs2OHWSZo8ebILpCkTjeBYQF4eDGEBZVS+8cYb1rNnT6eg14+CYSpFWrhw4ViZqKgoUwD6sssuc0Gz+IJj2un7ZVtt0/6jVrd0hF1RuWjscfgGAQQQQAABBBBAAIH0KMAaZOnxqjImBBBAAAEEEEAAAQSSUaBx/bpW1DMJ/+rb79vn34yz7bsibaAnaBaWPXvAs954441WvHhxU9ZLnTp1XJZLBkXbPE0ZYs8884y98sorNnfuXBs4cKDLHPvrr79cWUVlvtAQCGWBffv22ezZs132pQJgeg2peYNdKkOqQJjKLGZUXcT/Nb3e3nrrLQsLCzOVMU2o/bhmp01Zsd0qReS2u5tVtlzZKGuakBnPI4AAAggggAACCKRtgUyDPS1tD4HeI4AAAggggAACCCCAQEoL5Mubx5o3amCL//jTli5f5UovKnAWFvbvIJmCYeXKlTOVWlRWmTc45u1zjhw5rHXr1rZr1y5bvHixzZw50w4fPuzJUstnt99+u4WHh3s35SsC6VJAwSyt9efbFPhauXKlK5Go18Sff/5pyqzctm2b1ahRw/S6UdPrafPmzbZw4ULbvXu3aX2y6Ohol2E2b948u+WWW1wGme+xfb/3VE61ZdujbOjM5RaePYsNaF3DiuX977F9t+N7BBBAAAEEEEAAAQTSmwABsvR2RRkPAggggAACCCCAAAIpJJAtW1br1K6NHfKUWFz42x+2yPOv7mW1LDx33Gscxde12rVru/KKS5cudeUVGzdubJ07d/5XQC2+Y/AcAmlNQGVF33vvPatYsWJsVpjGoHXFHnjgARcsVolErdu3d+9eW7dunamkYpUqVVw2prYtWLCgLVq0yAXPZs2aZRMnTnTbtWvXzm666SaXRabtArUjJ6Jt2NzVtufoSbupfnmrVybCMv4vwzPQ9jyGAAIIIIAAAggggEB6Ecjg+VSaPjBGQwABBBBAAAEEEEAAAQQuWODTr7+1z0Z/5wJc0779wnIEyCRLzMEVFFBJRmXFfPLJJ1a1atXY3fS/LsePHzcF0JRtVr58efe81mPyz0qL3YlvEAhigVOnTln37t1d5ldERIQNHz7cSpQoYadPn7Znn33WFOx6+OGH7ZprrnH3uAJjKkOqsot58uRxWWLKJlPTGmTaR4EylVXU6+iGG26Id/RnPK+plz2ZYws27rFOVUtYv2ZV4t2eJxFAAAEEEEAAAQQQSE8CBMjS09VkLAgggAACCCCAAAIIpKLA91Nn2PBRX1qO7GH20jOPW4VyZc6rN1pDSeuRTZ8+3a2ZNGrUqNj9FRAYN26cTZgwwfbs2RP7eKlSpaxnz54ugOC7/lLsBnyDQJALrF692l5++WVbtWqVC44NGDDAKlWq5Eor6p5W0MxbTlFD2bRpk9t+yZIlVtizFuC9997rSpSqRKOCyAqUaT/ffQIR/BNzxkb/vslGL9lkl5csYE+0q2HZMp9b5jHQfjyGAAIIIIAAAggggEB6EaDEYnq5kowDAQQQQAABBBBAAIFUFqhUvpxFeNYNmzHnJ/th7jzr3rG9ZcmSOdG92r59u3388cdugr9Pnz6x2WMxMTH25JNPuuCYJv+1xpIyapRpoyCB1i3TGk5169ZN9LnYEIFgEVB5xAYNGthvv/3mgl9z58617NmzmwJn1113nVtvzLevWpuvZcuWtnHjRhdU+/nnny1r1qxWvXp1FxjT98qqTKht2HfYPvr5by1iZgOuqGqFwsMS2oXnEUAAAQQQQAABBBBIVwIEyNLV5WQwCCCAAAIIIIAAAgiknoDKHFYoX9aKFS5kf/y1wkaPm2B1alW3iAL5E9Wp+fPn24wZM0wBAAXIVHLuxIkT9sQTT5ieU0m5++67zwXL6tevb82bN7c2bdq4QIKyzipUqGBly5ZN1LnYCIFgEsiVK5ddddVVFhkZ6QJfCpYdPnzYBYm1/ph/UxCsadOmLpi8cuVK+/PPP12QuHLlypYtWzb/zf/184Hjp+zN2Sst6ni0PdTyUqtVqsC/tuEBBBBAAAEEEEAAAQTSu0DG9D5AxocAAggggAACCCCAAAIpK9CqRVN76M7b7JhnvbDHh7xk+6MOJKoDs2fPdmsvqWyissRULu7777+3n376yQXHXnnlFZc5puCAt2nb66+/3mXOaP/o6GjvU7Fftc4TDYFgF1CQ7PHHH3clQ3Xv65/Kimq9vUBNJRTvv/9+e/TRR93TX375pR08eDDQpuc8ptKKX/260VbvPmwtKxWxxuULnfM8PyCAAAIIIIAAAgggECoCBMhC5UozTgQQQAABBBBAAAEEUkhAayG1bNbYhj410GI8k/Hdbrrd1q7b4Cb84+tCgQIFLHPmzG49JZWYO3TokE2cONFTAS6D9erVy2rVquUCYf7HKFKkiNtGa5MpGKa1zLz/FixYYC1atLDevXu7Enb++/IzAsEkkDNnTnvwwQftgQcesNy5c9vevXvt9ddft6ioqIDd1OukW7duLrD22muvmQLGCbWl26Ns3oY9VrZALuteu4x77SS0D88jgAACCCCAAAIIIJAeBQiQpcerypgQQAABBBBAAAEEEAgCgQZ169gj991pmTJmtCdffNU2bt4Sb68UFFBpRZWaU1OJua1bt5qCBj169AgYHNN269evdwGxsLAwU3BOa5INHz7cli9fbi+//LInSBdjKj2nNZpoCKQFAa09pmwyBcDmzZtn/fv3d/d5oL4rgHz11Ve7dcwCPe/7WNSxU/bpwvV28nSM9WlQwYqw7pgvD98jgAACCCCAAAIIhJgAAbIQu+AMFwEEEEAAAQQQQACBlBLI6AmMNW1Yz9559QXPWknH7db7Btj6jZvjzCRTxky/fv0sPDzcdVGBL5VM1FpL3scC9b18+fJWokQJq127tqns3LRp0+zTTz+122+/3Xbv3u0CYw8//LALNgTan8cQCDYBbybliBEjrGjRorZixQp77LHHbN26dQFfPwqSJdROe7I5P1u0zrYeOGbX1CpldUtHJLQLzyOAAAIIIIAAAgggkK4FMg32tHQ9QgaHAAIIIIAAAggggAACqSpQMKKAVShXxhb9/ofNWbDQqlT0ZK4UKphgn1QmccKECVa4cOHYrLJAOxUqVMjatWvnssSURZY3b16bO3euK7eowNrbb79tKt9IQyCtCeTPn9+VFl2zZo3LIFPJ0IIFC1qZMmXizKiMa4yz1+60b//capcWyWO3Nqpk2bNkimtTHkcAAQQQQAABBBBAICQEyCALicvMIBFAAAEEEEAAAQQQSF2B+nVq2/tvDLV9+6Ps/seeti3bdiTYoXz58rnsmZ9//tmVWoxvBwXCtL0yzl566SVXnlHlFo8cOWLKHtN6ZjQE0pqAMsMuueQSlxFZr149lxE5ZMgQmzlz5nkPZawnOBbtySLrXbe85c2R9bz3ZwcEEEAAAQQQQAABBNKbABlk6e2KMh4EEEAAAQQQQAABBIJUII+nhGKdWjXstz+W2uQZs6xC2bJWvGiROHubLVs2y5o1q1uDSeUWlSl29uxZ27Rpk40ZM8YFw0qXLh27/65du2zgwIG2atUqq1Spkr3xxhsu2yYqKsoaN25sKuFIQyAtCihQ1qBBAxfwVTbZgQMHXNakypgm1I6dOm0vzlhmG/Ydsb6NK1qzinG/5hI6Fs8jgAACCCCAAAIIIJCeBDJ4/gfzbHoaEGNBAAEEEEAAAQQQQACB4BXQ/37s2BVpve+4z3LmCLO3XhpiFcuVjbPDp0+fto8//thl0ChYpqDZP//8Y8ePH3fBr7Fjx8auLTZgwAAXTNOaTa+99ppVrFjRtP+pU6csZ86ccZ6DJxBIKwLKkJwzZ45VqFDBtPZeQk3/sz9zxQ57a95qq1OygD1+VXXLkTVzQrvxPAIIIIAAAggggAACISFAgCwkLjODRAABBBBAAAEEEEAguAS2bt9hjzz9vO0/cNBefHKg1b2spilLJq42b948mzRpku3du9cyZ85sxYsXt169erl1xw4fPmyvv/66TZ061a019swzz1jDhg3POZS2OXjwoAumaT0ylV+kIZDeBbZFHbMh05baUU8W2ZAOta1iofD0PmTGhwACCCCAAAIIIIBAogUIkCWaig0RQAABBBBAAAEEEEAgKQW27dhpN/S73/Lny2sf/ecViyiQP97DKxtMa4opuKWMMH1VNtmwYcNcyUWtQfb8889b3bp1Y4+jTLNRo0aZ1jHTvspCU4DsxhtvtEaNGsUblIs9CN8gkAYFok/H2PNTl9nv2/db/yuq2hWVi3ru9zQ4ELqMAAIIIIAAAggggEAyCSRcsDyZTsxhEUAAAQQQQAABBBBAILQFShYvZhO+HOHWFet+8x22Z+++eEGUOaYgWHh4uAuOqVzj5MmT7dtvv7UsWbLYPffcY5dffnnsMbZu3WoPPPCAjRw50rSGmcrTnThxwq1R9thjj9mIESNcCcbYHfgGgXQiEHPmrE1cts2W7NhvrTyBsSurEBxLJ5eWYSCAAAIIIIAAAggkoUCmwZ6WhMfjUAgggAACCCCAAAIIIIBAogW0pli5MqXsp18W2/JVq61RvcstLHv2RO2/a9cu07pjyiJ78MEHrUuXLpYx438/A6jnHn30URcMU7bZfffdZ7feeqt17NjR6tevb5GRkTZt2jQLCwuzGjVqkEmWKHE2SisC6/ccsY9++dvCMmey+1pcannCsqaVrtNPBBBAAAEEEEAAAQRSTIAMshSj5kQIIIAAAggggAACCCDgL6B1x+rXqW2DBz5k6zZssqH/edeT6fWP/2YBfz527JidPHnSZY+1atUqdl0xZYq9+eabtm7dOitcuLB9+umndu2111rFihWtTJkyrrTioEGDrGjRou651atXBzw+DyKQFgX+iTljny1eZwdPRFu/JpWsVP6caXEY9BkBBBBAAAEEEEAAgWQXIECW7MScAAEEEEAAAQQQQAABBBISaFy/rt11603214qV9p8PPrZjnrXDEmqlS5e25s2bu9KJyhDbs2eP2+WXX36xxYsXW+7cuU2BMAXF/Fvx4sVdOUatUbZhwwb/p/kZgTQpoODYF4vX2187DlrHaiWsSYUiaXIcdBoBBBBAAAEEEEAAgZQQIECWEsqcAwEEEEAAAQQQQAABBBIU6NqhrXVq29qm/vCjfTdhSoLbZ82a1YYMGWJXXHGF5cmTxwoWLOj2WbRokSnwddVVV1mDBg0CHkeZawqSnT592jZv3hxwGx5EIK0JrNtz2Gas2WVFwsOsc43Saa379BcBBBBAAAEEEEAAgRQVyJyiZ+NkCCCAAAIIIIAAAggggEAcAgpa3dO3j0V71hQbNfo7y5gpk13XtaMroRjHLm4NMS2rrHXItL/aqlWr3NcOHTrEPuYe8PmPAmMqrah9IiIifJ7hWwTSpsDhk//YJwvXWcyZs3anp7RiofDEreWXNkdLrxFAAAEEEEAAAQQQuHgBMsgu3pAjIIAAAggggAACCCCAQBIK3HFTb6tx6SX22dff2uyfFiR45LCwMAsPD4/dLpMnsKZWpEjc5eWUNbZs2TLLli2bFStWLHZffTNq1Cjbv3//OY/xAwLBLvD1rxtsVeQh61y9pNUuVSDYu0v/EEAAAQQQQAABBBBIdQECZKl+CegAAggggAACCCCAAAII+ArkzJnDnnrkAStZvJh9OOpL+3vDRt+nE/y+Zs2abhutQxaoRUVF2dtvv2179+61ypUrW+3atd1mR48etaFDh9q7775rI0eODLQrjyEQlAILNuy2aat3Wq0S+a1TzVKW8X/ZlEHZWTqFAAIIIIAAAggggECQCBAgC5ILQTcQQAABBBBAAAEEEEDg/wUK5M9nzw96xCLy57fHh7xkK1av/f8nE/iuRYsWli9fPhfk2rRp0zlbb9261R5//HFbuHCh5c2b155++mmXfXb27FmXOTZhwgT3uEovfvDBB7ZixYpz9ucHBIJN4LSnpOL3f22z6Jgzdl2dshaePUuwdZH+IIAAAggggAACCCAQlAKZPPX6Bwdlz+gUAggggAACCCCAAAIIhLRA7ty5rGK5Mjbjx59s2arVVu+yWpY7V84ETQoXLmynTp2y+fPn26xZs9waZsoamzx5sr3++uu2ceNGy+8JvA0aNMiUbab1y8aNG2fvvfeenTlzxv1bv369/frrrzZx4kSrWrWqlSxZMsHzsgECKS1wxhPY/XbJJpuzLtJurlfOrqhybrnQlO4P50MAAQQQQAABBBBAIC0JZPB8UvJsWuowfUUAAQQQQAABBBBAAIHQEli1dp0NeeVNU8Ds6UcedKUXExKIjo52wa1PPvnE9uzZE7u5MsOqVatmt99+u9WvX9/0v0PfffedvfnmmxYTE2MNGjSwbt26mdY1U3BNATIFx5RNVrBgwdjj8A0CwSCwOvKgPTXpTyudP5c926GW5cpG9lgwXBf6gAACCCCAAAIIIJA2BAiQpY3rRC8RQAABBBBAAAEEEAhpgQWLfrPnX3vLypUpZS8+NdDy5smTKI8TJ07YpEmTXKlEBcdUfrFJkyYuq0zBMa1T9sgjj7iMs/bt29sTTzxhWbNmdcfWGmUqwdiqVSvr3LmzZc6cOVHnZCMEUkLg5D8xNnTGMvtje5Q93rqaNSpfOCVOyzkQQAABBBBAAAEEEEg3AgTI0s2lZCAIIIAAAggggAACCKRfgdOnT9u4ydPtk6/GWJP6de3+frd6yi3muuABq5Ti3Llz7bnnnrPjx4+7rLF7773Xcub8/xKOCqDpvFmykJVzwdDsmGwCizbusdd+XGk1i+WzAZ4AWVgWArjJhs2BEUAAAQQQQAABBNKlAH9Bp8vLyqAQQAABBBBAAAEEEEhfAsre6tGpvR08dNhGj5vgMsjuvu0mU1bYhbQ1a9bYK6+8YkePHrVOnTrZgAEDLFOmTOccSscmOHYOCT8EicCB46fs/flrLdxTUvGR1tUte5Zz790g6SbdQAABBBBAAAEEEEAgqAUyBnXv6BwCCCCAAAIIIIAAAggg8D+BjBkz2rVdO1rDunVsysxZNmvu/AuyWb16tQ0aNMj2799vjRo1srvuuutfwbELOjA7IZACAtExZ+ybJZvtwIlou7paSYJjKWDOKRBAAAEEEEAAAQTSpwABsvR5XRkVAggggAACCCCAAALpUiBPeG4b+MBdVqpEcRs2/BNbu27DeY9z0aJFtn37dmvQoIErsRgREXHex2AHBFJLYPP+o/bT+t1WJl9Ou7Jy0dTqBudFAAEEEEAAAQQQQCDNC7AGWZq/hAwAAQQQQAABBBBAAIHQE1jz93p76sVXLTx3LnvxqcescKGC54UwYcIEu/zyy6148eLntR8bI5CaAqdOx9jQGctt+c4D9lTbGlarZIHU7A7nRgABBBBAAAEEEEAgTQuQQZamLx+dRwABBBBAAAEEEEAgNAUqli9rN1zTzTZu2Waffv2tnTlz5rwgOnfuTHDsvMTYOBgEZq3eYb9v3W9tqhQjOBYMF4Q+IIAAAggggAACCKRpgcxpuvd0HgEEEEAAAQQQQAABBEJSIFOmTNb56qts1+7d9s2EKVaqeDHr6VmfTI/TEEiPAls9pRW/XrLFiuUJs041SqbHITImBBBAAAEEEEAAAQRSVIAMshTl5mQIIIAAAggggAACCCCQlAJ9el1rDevWsZFfjrF5C3+1s2fPJuXhORYCQSMww5M9dvBEtHWoWsIKh4cFTb/oCAIIIIAAAggggAACaVWAAFlavXL0GwEEEEAAAQQQQAABBCx79mx2+03X2xlPYGzkF19b1MGDqCCQ7gRWRx608cu2Wa3i+axTzVKWMUOGdDdGBoQAAggggAACCCCAQEoLECBLaXHOhwACCCCAAAIIIIAAAkkqUKZUSXvtuSft2PET9uzLb9r+qKgkPT4HQyA1BQ4cP2UfzFtr+cKyWu+65VKzK5wbAQQQQAABBBBAAIF0JUCALF1dTgaDAAIIIIAAAggggEBoCtSuXs1uuq6HrVq7zj767Gs7eepUaEIw6nQloIqhs9dE2gbP+mOtKhe1SoXzpKvxMRgEEEAAAQQQQAABBFJTgABZaupzbgQQQAABBBBAAAEEEEgygVbNm1jT+nVt3i+LbP7CxUl2XA6EQGoJ7D583Kav3m4RObNZW8/aY5kyUloxta4F50UAAQQQQAABBBBIfwIEyNLfNWVECCCAAAIIIIAAAgiEpECunDntoXtut8oVytuwD0faH0uXh6QDg04/Ap8tWm97jpyyu5pWtqJ5wtLPwBgJAggggAACCCCAAAJBIECALAguAl1AAAEEEEAAAQQQQACBpBEIz53bHrjzNsudK5e9/NZ7tmXb9qQ5MEdBIAUFVFrxp7932U8b9liT8gXt8tIRKXh2ToUAAggggAACCCCAQGgIECALjevMKBFAAAEEEEAAAQQQCBmBUiWK2y29etreqP02avR3ITNuBpp+BA6djLYJy7dZ1syZrFO1kpRWTD+XlpEggAACCCCAAAIIBJFA5iDqC11BAAEEEEAAAQQQQAABBC5aIGPGjNa6ZTPbFbnHPh39rRUpVNBuu+E6y5Qp00UfmwMgkBICM1busLV7DluvOmWtStG8KXFKzoEAAggggAACCCCAQMgJECALuUvOgBFAAAEEEEAAAQQQCA2BHl2utq07d9qY8ZOsTKmS1rpFU8uQIUNoDJ5RplmBdZ7A2IRl2+ySwnmsa63SaXYcdBwBBBBAAAEEEEAAgWAXoMRisF8h+ocAAggggAACCCCAAAIXJJAjLMz63ni95ciezb4eO8FORUdf0HHYCYGUEjj5T4x9tni9RcfEWO+65SxHVj7TmlL2nAcBBBBAAAEEEEAg9AQIkIXeNWfECCCAAAIIIIAAAgiEjIDKKz71yEO2b3+UvfTGO3bs+ImQGTsDTXsCy3cesLW7D1u1ovmssieDjIYAAggggAACCCCAAALJJ0CALPlsOTICCCCAAAIIIIAAAggEgUCd2jXsqiua27yFi232TwuCoEd0AYHAAlOWb7MTniyyG+qRPRZYiEcRQAABBBBAAAEEEEg6AQJkSWfJkRBAAAEEEEAAAQQQQCAIBTJlzGjdO7az4sWKekotfm/rN20Owl7SpVAX+HHNTvt9W5R1q1XKKhQKD3UOxo8AAggggAACCCCAQLILECBLdmJOgAACCCCAAAIIIIAAAqktULRIYXvswXvs6NFjNnzUl3b69OnU7hLnRyBWYO+Rkzbmj81WJDy7ta5SLPZxvkEAAQQQQAABBBBAAIHkEyBAlny2HBkBBBBAAAEEEEAAAQSCSKBqlUrWqV0b+/3Pv+wrTyZZTExMEPWOroSywKJNe23X4RN2ZaWiVjxfzlCmYOwIIIAAAggggAACCKSYAAGyFKPmRAgggAACCCCAAAIIIJDaAt08pRYvrVzJxk+abivX/J3a3eH8CNjRU6dt8optViw8zNpWLWEZMEEAAQQQQAABBBBAAIEUESBAliLMnAQBBBBAAAEEEEAAAQSCQaBA/nzWr88NdvjoURv5xRhPqUWyyILhuoRqH86eNRv9+0bbceiEXV+3nOXLkTVUKRg3AggggAACCCCAAAIpLkCALMXJOSECCCCAAAIIIIAAAgikpkD1S6vYnbfcaKvW/m3vfvypnToVnZrd4dwhLPD3nkM2Ydk2q1k8n9UrHRHCEgwdAQQQQAABBBBAAIGUFyBAlvLmnBEBBBBAAAEEEEAAAQRSWaBNy2ZWrkxpm/7jXFu2anUq94bTh6LAPzFnXHAsxpNG1qFaCQvLmjkUGRgzAggggAACCCCAAAKpJkCALNXoOTECCCCAAAIIIIAAAgiklkCe8Nx2f79bLVvWrDZ81Fd24OCh1OoK5w1Rgc37j9rS7VFWt1QBq1OK7LEQvQ0YNgIIIIAAAggggEAqChAgS0V8To0AAggggAACCCCAAAKpJ3Bp5Yp2a+/rbMOmTfbuiFGp1xHOHHICZz1ZY58tWm/6H/IHWl5qWTPzv+YhdxMwYAQQQAABBBBAAIFUF+Cv8FS/BHQAAQQQQAABBBBAAAEEUkugdcum1qR+PZs1d75N/eFHU+CChkByCugWm7N2l/2544A1r1jE8ufMlpyn49gIIIAAAggggAACCCAQhwABsjhgeBgBBBBAAAEEEEAAAQTSv0BY9ux2w7XdLCx7Nhs9boLtizqQ/gfNCFNV4NDJaJu2aqflzJrJWlcpmqp94eQIIIAAAggggAACCISyAAGyUL76jB0BBBBAAAEEEEAAAQSsUvly9uCdfW1n5B57e/hIi46ORgWBZBP4dfNeW7vnkHWtUcrKROROtvNwYAQQQAABBBBAAAEEEIhfgABZ/D48iwACCCCAAAIIIIAAAiEg0OaK5taqeROb98tiGzd5msXExITAqBliSgvsO3rKxizZbGXy57Kra5RM6dNzPgQQQAABBBBAAAEEEPARIEDmg8G3CCCAAAIIIIAAAgggEJoCGTJksGu7drLcuXLaxGk/2O69+0ITglEnq8Dk5Vts37FT1q12acudLUuynouDI4AAAggggAACCCCAQPwCBMji9+FZBBBAAAEEEEAAAQQQCBGBsqVL2p233GiRe/baV9+OD5FRM8yUEti874jNXrvbLimcxy4rkT+lTst5EEAAAQQQQAABBBBAIA4BAmRxwPAwAggggAACCCCAAAIIhJ5A2ytbWMN6dWza7Dn286LfQg+AESebwIxVOyzq+Cm39lh4WNZkOw8HRgABBBBAAAEEEEAAgcQJECBLnBNbIYAAAggggAACCCCAQAgIZMqUyXr36Gq5cuayz8aMtf1RB0Jg1AwxuQU27T9ic9ftthYVClv9cgWT+3QcHwEEEEAAAQQQQAABBBIhQIAsEUhsggACCCCAAAIIIIAAAqEjUKl8WWvTspmt37TJfpgzL3QGzkiTReDEPzE2fP5ay5I5o3WvXSZZzsFBEUAAAQQQQAABBBBA4PwFCJCdvxl7IIAAAggggAACCCCAQDoWyJw5s13fo4sVK1zYxk+dYQcOHEzHo2VoyS2wZOs+W737sF1WsoCVyJ8zuU/H8RFAAAEEEEAAAQQQQCCRAgTIEgnFZggggAACCCCAAAIIIBA6Avnz5rG7+t5sR44etdfe+dBOnDwZOoNnpEkmEHPmrM1as8uyebLHetQubVkz8b/gSYbLgRBAAAEEEEAAAQQQuEgB/jq/SEB2RwABBBBAAAEEEEAAgfQpUKvqpVatSmX77c+/7Pc/l6XPQTKqZBVYv+ew/bFtv3WsVtJK5CN7LFmxOTgCCCCAAAIIIIAAAucpQIDsPMHYHAEEEEAAAQQQQAABBEJDIEeOMLu197WWNUsW++b7SXbmzJnQGDijTBKBU561x0b+8rcVypXd2lYtniTH5CAIIIAAAggggAACCCCQdAIEyJLOkiMhgAACCCCAAAIIIIBAOhOoUqmC9erR1ZatXG2jx05IZ6NjOMkpsHzHAVseecguK1XA8uXIlpyn4tgIIIAAAggggAACCCBwAQIEyC4AjV0QQAABBBBAAAEEEEAgdAS6dLjKql1S2T4d/Z2tWbchdAbOSC9Y4NTpGJu1dqdlzpjRWlcuZpkyZrjgY7EjAggggAACCCCAAAIIJI8AAbLkceWoCCCAAAIIIIAAAgggkE4EwrJntx6dr7Z/oqPt67Hf26lTp9LJyBhGcglEHjphSz0ZZC0rFrbyhXIn12k4LgIIIIAAAggggAACCFyEAAGyi8BjVwQQQAABBBBAAAEEEEj/AhkyZLAm9etZ00b17fc/l9mqtevS/6AZ4QULnDl71sYt3WxnPV+vu7ycZfTcPzQEEEAAAQQQQAABBBAIPgECZMF3TegRAggggAACCCCAAAIIBJlAliyZ7aG7+lq+vOH22rsfWtSBg0HWQ7oTLAJrdx+yeRv2WONyha1onrBg6Rb9QAABBBBAAAEEEEAAAT8BAmR+IPyIAAIIIIAAAggggAACCAQSyJc3r13dppXtitxj46dMt5iYmECb8VgIC5yOOWMzVu60LJkyWqvKRUJYgqEjgAACCCCAAAIIIBD8AgTIgv8a0UMEEEAAAQQQQAABBBAIEoF2rVpYlYrlbfqsObZl2/Yg6RXdCBaBnYeO2x/b91v1onk9a4+FB0u36AcCCCCAAAIIIIAAAggEECBAFgCFhxBAAAEEEEAAAQQQQACBQAJ58+Sxvjdeb/s9JRa/+Ha8nTlzJtBmPBaiAjNX77QDx6OtR+0yli1zphBVYNgIIIAAAggggAACCKQNAQJkaeM60UsEEEAAAQQQQAABBBAIEoHLala3rle3tXk/L7Kffl4cJL2iG6ktsMuTPTb370hrUr6QXeLJIKMhgAACCCCAAAIIIIBAcAsQIAvu60PvEEAAAQQQQAABBBBAIAgFOrVrbREF8tuIL762KE82GQ2B7//aav941iDrWL0kGAgggAACCCCAAAIIIJAGBAiQpYGLRBcRQAABBBBAAAEEEEAguARKFi9mrZo3sT1799mi35YEV+foTYoLbNl/1BZs3GOVCodb6fy5Uvz8nBABBBBAAAEEEEAAAQTOX4AA2fmbsQcCCCCAAAIIIIAAAgiEuEDGjBntum6drUD+fDZ+yowQ12D4CzbstkMn/rG2lxa3nNkyA4IAAggggAACCCCAAAJpQIAAWRq4SHQRAQQQQAABBBBAAAEEgk8gV66c1rtHF9u0dat9MWacnT17Nvg6SY+SXeDYqdM2f/1uq+5Zd6xRucLJfj5OgAACCCCAAAIIIIAAAkkjQIAsaRw5CgIIIIAAAggggAACCISgQJOG9a18mTI2dtIU27J1ewgKMOTJy7fa3mOnrGedMpYhAx4IIIAAAggggAACCCCQVgQIkKWVK0U/EUAAAQQQQAABBBBAIOgE8uYJt45tW9mBQ4dtxpyf7MyZM0HXRzqUfAKRh07Y6D+2WMWC4VaxUHjynYgjI4AAAggggAACCCCAQJILECBLclIOiAACCCCAAAIIIIAAAqEk0KxRA6tYrozNmjvf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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0, + "referenced_widgets": [ + "0806477acc4149a980271b600b0b27e6", + "8b258df3426f4781afed10499392c225", + "7145e6a71760467eb8797864f0e5d2a0", + "9c5380da6d794a228ac12ecab0d949ef", + "e6256c3b21914adc871cf4db6baf7fee", + "af651b267e5b4b11a2605b02f5a75ff3", + "f86c993cec21489983df88cee2ab5b2a", + "8348424c59c94f279f061d2ba339b17d", + "5c2e8b16eaba46d7ad63a4723a0e6c95", + "db764534dfec4bc3a9ef40f37c154129", + "ff2e1f75ed3a42e9882c2ce5e5d748c9", + "c4d4cc4bf5c54d6691135b25ba4d0c84", + "57c5c601d72d4bd8a30779500c0f63f1", + "678ff0b0994a40a982091b2d13f57c4a", + "958d1cb2fff3485a86ad5f3d9456d579", + "3fe5027f1c994fc4ba3c22f245604c13", + "050f31f028794d49bdad2e33c5e1e32e", + "a2954cc253fa4a2f9ad430ee12cec117", + "1b06375f7c054d05aac5766732801ad9", + "399256ab248e403ba3f26d5bd91b091c", + "3c19457d425b4574817572b38aa17d86", + "7a9a3f418d6a409cba2643daa11ec233", + "28ace3f50b3c4a25a7fc1b7f6eab7814", + "d2bc18ff98e142c58fdf18a9075d4c2b", + "98d22f88193c47939b93d552a3041447", + "66079ff3dbe043df86ba4c8cddd7ffb7", + "245bdf024f754fa1bbb8bedc5b59cfef", + "4f5490021107448cba0795253a892a71", + "47f69ff14b9a4021bdfe15d532a65753", + "3f92018b112e4213a1ee9b3d810828e5", + "4f4bb6f685a44d318827eecfd9d25215", + "a4acb870019b4b1daabbc92ce84ed88b", + "c48a252f55d64e78aadc927d94de4b4f", + "380f4a19bf51442e8bc93b77abe405d5", + "798d0ce58f28445cb91bcd4af75ad379", + "09a5785134684029b1cff1bc7510c534", + "f280bf45be9947bfa83e5d2ad1ffdd48", + "5b08d7a0cd37421fac9dd182c7e803a3", + "0c409a70fd6b4c42a75e575398722229", + "f74aa3ab9aff44f89396316e51f9774a", + "354a8042212b42b5ab48116c07be8c4a", + "ce982e0638b4403dba8bb46e8402d506", + "5c32f4f07b8241eb810c6b7d3bf0d516", + "98ef86cf824a4d7ca12d8cb76b22bbb6", + "772165f70bd347009bdbdc989a3610e5", + "e7e96edf087640c5a618deb016e4d277", + "9a481076f28e4e9098fca0d75d5e44f3", + "efcf3c8ce7424604b4726a5fdf8f9a97", + "ac0b18ff7a1c40b5b50326d891f5e5af", + "96e6a9c132cb4e068c01d819c9c53951", + "87ccb50738954e6d94e2d75f8194215f", + "096ad7b5b4844287b88f2b76e5cf107b", + "5e4d0d5329cd4f8f83fcef7dfd413eb1", + "d1ae873ccd9b459b8c8074d0cf87ff84", + "820e68b984f6428a8bff72a178447b41", + "d61489f2e3b34790905f0d2f5e32f17f", + "a81ff8d9fc6142188423abf029103491", + "ef3be25a72514885a169017b63a4212f" + ] + }, + "id": "a6uPF7X_a2sB", + "outputId": "d43ae6b2-19b9-4399-9879-fd98f37f995e" + }, + "outputs": [], + "source": [ + "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n", + "Datasets(db).load(\"OPEN_INTELLIGENCE_ANGOLA\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mNWLGGTUipj" }, "source": [ - "The `arangodb_graph_to_rdf` and `arangodb_collections_to_rdf` methods return two objects:\n", + "#### via Graph Name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0, + "referenced_widgets": [ + "e7b1063bfccb405ea38b0fac7e2ab121", + "bb8e13a4f24f4a33a7b72454d38e6092", + "a903e7ef6e0946168d5422fba725bfbb", + "96d344126fbe41159cf025667fbce0c5", + "7fa97decd4204eb498b44554d9b2ac1a", + "38e979b0cf94465388eeb0c2e1d37214", + "025f0dba6472458da784d68dd82f6b05", + "6d2a7f63e099467d86af116e97de71a8", + "f0ae785301df4dd08619c600dc98d04d", + "eaeea446d3f74ed6adabfa5646ac05a2", + "72f9d06266ea4e97849c170c48308c77", + "b0c72633943841ec8911af176a347b21", + "22644f41d98444ad8f897fb1b940b854", + "5c60b07c9c5c424cbd266d3c2bc7189d", + "7e72dc811b934c2883020de74c82625a", + "695b886aee124d78ae582f914b88e26a", + "6c191d39e72f4dfc90443e57184cca27", + "e815564404004a3391d274458502ce9c", + "4106df416f7c47a1a0b6ab9e954acdab", + "9d5e41d68dd9479db77ef9bef14835de", + "52abd56876a0434abe9570679bbdb57b", + "6fecb562ef6c4e51be7bd0b42108c15e", + "666341c39bc3491086c936250956e6c4", + "939df2cace1e4c92bed3c64acf556698", + "7d4b161694aa424489ec20c93023128d", + "423019e8f56e4579a18e1b08bb216944", + "d062be96b4164b0fa74f3492465b7a63", + "5c6159ac9941405ea2399930e3385856", + "a516048f6b254c4c89924978719cd530", + "d8e05175e7e4468783b8f748cd69440b", + "ced9a003eb3c4db0b9c67cf5d4eba3ec", + "4c00c2738c77446291a8a88da8061603", + "bd8b2fd62451435a96df5db182c28f23", + "5c5d3d5139c44a7abc6616d674aab82c", + "2cb971a1d4ed49b3b1de2f1b062b40a1", + "2e6aa556b9fd44f68347677bea11caa3", + "cfbd898fda734c0585f5c9901a4c629f", + "8fb1525ca3744f33a0be9f712507cb03", + "92bb5e694f98436bad05b576e6aa1aab", + "6efa083feddf4ce497059534916b2b43", + "dcb2d51e15c7495fa7394660acfada07", + "0bf8f16e94f848d6bb465dc307c0e90b", + "76da0198443840b6b36d6f4ff9fb9aea", + "5e6618e3da7446fa8be8fb0c38a44935", + "a2d650f2905a4544a54b349d32c6d745", + "5b141553b59d4a64b567117a48a0f05d", + "2d19bd459bf54b1197a1fd18c47f4b05", + "5542d4fbb1294b63a5e7dee5f4af5fa0", + "a78281fc9c554c57852e29bc09ac8bea", + "80d8531d226f4b6c9d144aaea20130e3", + "af1f098d2a8f46f9b517ac359687a2ab", + "b126e7213e4f4630af51f1c3fe526e58", + "1088d2e6621a41c8a912520fb9206879", + "8bf0a3e6c09545d7a55487783e0b82c9", + "3cbbfb7c4c254dd995bc2d10e2429a4d", + "005f4ea3e18243d886f4264a8e328bcf" + ] + }, + "id": "rA5dBseDaiQB", + "outputId": "6979c566-f6c2-4c41-d98c-183b1317d728" + }, + "outputs": [], + "source": [ + "rdf_graph = adbrdf.arangodb_graph_to_rdf(name=\"OPEN_INTELLIGENCE_ANGOLA\", rdf_graph=Graph())\n", "\n", - "1. The RDF representation of the ArangoDB Graph, i.e `rdf_graph`\n", - "2. Another RDF Graph mapping the RDF Resources to their designated ArangoDB Collection, i.e `adb_mapping`.\n", + "print(len(rdf_graph))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-FTakjQKUoWW" + }, + "source": [ + "#### via Collection Names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0, + "referenced_widgets": [ + "ad0e243808cb46aa8605d8929e5129e2", + "15f7cd15b0904731a4ce5daaa81f560b", + "c49b5805f1bb4cd79fd87111fbd96118", + "23f1b6f7b7564c78aaf32ed5b7f382a2", + "4c8a803a162a47fca5b6484485a50168", + "6060a8afe98e41388fc3ca928af0d70d", + "2f4743884cf64364831cd82a9f2b7ed3", + "9222e80943b64fdaafbf0081aa501800", + "d1bd0d1096f042db9f2e41892c8855ff", + "67989ac8ad2a4f1a9919736d384cd294", + "ee1ee485ba5d4618906048b066e5b6b9", + "d6fa32c7e0794b78972d889c2ce55546", + "45883547766c4981b798206e55a5c2f9", + "95725b5bc05e4576b867b6066c5cd0b4", + "6e3a7048cf634fe4b8d16547ffe89d7d", + "5e4096f76e344148b57a688b2e9fe8b9", + "0769e25469e44c0f97b9d1f9ad2abf5d", + "50e0d72760df48cdbd0ab58a118f3b83", + "00da493dbd004b858625ed4fb1e2556d", + "48de9d4c770749dc933ec65ef35e9ce6" + ] + }, + "id": "TDny1v4sdelB", + "outputId": "346d2e1a-02b9-4918-8370-a0305ab66f22" + }, + "outputs": [], + "source": [ + "rdf_graph = adbrdf.arangodb_collections_to_rdf(\n", + " name=\"OPEN_INTELLIGENCE_ANGOLA\",\n", + " rdf_graph=Graph(),\n", + " v_cols={\"Event\", \"Actor\", \"Source\"},\n", + " e_cols={\"eventActor\", \"hasSource\"},\n", + ")\n", "\n", - "The second graph, `adb_mapping`, can be re-used in the RDF to ArangoDB (PGT) process to maintain the Document-to-Collection mappings." + "print(len(rdf_graph))" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "UCQ9ppnUQa7e" + "id": "qddfFNtaUpoo" + }, + "source": [ + "#### via MetaGraph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0, + "referenced_widgets": [ + "c09d4aa3bbb440eea986e872479dd746", + "04e0e2fbdcbf43bdb6fd3a1cb1b9a5ba", + "5c3399c807834637b5b872fdec6a126a", + "a27c2cbe14eb40aa8b071a031c694220", + "94e3ac81d4924b1699a1801aaffd5349", + "6691ef61f8e743e8a274bf0710c84673", + "85ee2b46acbe42cb838ff89ee8457607", + "c627a1455abe47ac921d1bd841cdd9c2", + "008a2c05cf294ef0a8be167b1a2433e9", + "a6fb760fd98d448b990b6a9636df4894", + "fe58b0d093f24f3da92c546038fe1735", + "de3564cef3e44c2fbc0a25953d1b4500" + ] + }, + "id": "kQSN13xdeH1X", + "outputId": "497885bc-1bdd-4a10-986f-387287b807cc" + }, + "outputs": [], + "source": [ + "rdf_graph = adbrdf.arangodb_to_rdf(\n", + " name=\"OPEN_INTELLIGENCE_ANGOLA\",\n", + " rdf_graph=Graph(),\n", + " metagraph={\n", + " \"vertexCollections\": {\n", + " \"Event\": {\"date\", \"description\", \"fatalities\"},\n", + " \"Actor\": {\"name\"}\n", + " },\n", + " \"edgeCollections\": {\n", + " \"eventActor\": {}\n", + " },\n", + " },\n", + ")\n", + "\n", + "print(len(rdf_graph))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RGivoIz5Uv_-" + }, + "source": [ + "# Round-Tripping" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s900bHJQinK3", + "outputId": "25e0dae1-e450-4576-aed3-38d1d4223dfa" }, + "outputs": [], "source": [ - "#### Non-native" + "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(\"OPEN_INTELLIGENCE_ANGOLA\", drop_collections=True, ignore_missing=True)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "mYZIEAzhQ5CO" + "id": "4PzrROxlU0TM" }, "source": [ - "Non-native: An ArangoDB Graph that originates from an RDF Context, which has been brought over via one of the `rdf_to_arangodb` methods (RPT/PGT)." + "#### RDF -> ArangoDB -> RDF" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "orwoPEIOQjHO" + "id": "oZNYvQxDgTvw" }, "outputs": [], "source": [ "data = \"\"\"\n", - "PREFIX : \n", - "PREFIX rdf: \n", - "PREFIX rdfs: \n", - "PREFIX xsd: \n", + "@prefix ex: .\n", + "@prefix rdf: .\n", + "@prefix rdfs: .\n", "\n", - ":The_Beatles rdf:type :Band .\n", - ":The_Beatles :name \"The Beatles\" .\n", - ":The_Beatles :member :John_Lennon .\n", - ":The_Beatles :member :Paul_McCartney .\n", - ":The_Beatles :member :Ringo_Starr .\n", - ":The_Beatles :member :George_Harrison .\n", - ":John_Lennon rdf:type :SoloArtist .\n", - ":Paul_McCartney rdf:type :SoloArtist .\n", - ":Ringo_Starr rdf:type :SoloArtist .\n", - ":George_Harrison rdf:type :SoloArtist .\n", - ":Please_Please_Me rdf:type :Album .\n", - ":Please_Please_Me :name \"Please Please Me\" .\n", - ":Please_Please_Me :date \"1963-03-22\"^^xsd:date .\n", - ":Please_Please_Me :artist :The_Beatles .\n", - ":Please_Please_Me :track :Love_Me_Do .\n", - ":Love_Me_Do rdf:type :Song .\n", - ":Love_Me_Do :name \"Love Me Do\" .\n", - ":Love_Me_Do :length 125 .\n", - ":Love_Me_Do :writer :John_Lennon .\n", - ":Love_Me_Do :writer :Paul_McCartney .\n", + "ex:Monica ex:employer ex:ArangoDB .\n", "\n", - ":McCartney rdf:type :Album .\n", - ":McCartney :name \"McCartney\" .\n", - ":McCartney :date \"1970-04-17\"^^xsd:date .\n", - ":McCartney :artist :Paul_McCartney .\n", + "ex:Graph1 {\n", + " ex:Monica a ex:Entity .\n", + " ex:Management a ex:Skill .\n", + " ex:Monica ex:name \"Monica\" .\n", + " ex:Monica ex:homepage .\n", + " ex:Monica ex:hasSkill ex:Management .\n", + " ex:Monica ex:dateOfBirth \"1963-03-22\".\n", + "}\n", "\n", - ":Imagine rdf:type :Album .\n", - ":Imagine :name \"Imagine\" .\n", - ":Imagine :date \"1971-10-11\"^^xsd:date .\n", - ":Imagine :artist :John_Lennon .\n", + "ex:Graph2 {\n", + " ex:Programming a ex:Skill .\n", + " a ex:Website .\n", + " ex:Monica a ex:Person .\n", + " ex:Person rdfs:subClassOf ex:Entity .\n", + " ex:Monica ex:hasSkill ex:Programming .\n", + "}\n", "\"\"\"\n", "\n", - "g = Graph()\n", - "g.parse(data=data)\n", + "rdf_graph_original = get_graph(data, True)\n", "\n", - "# Selecting RPT or PGT for this example does not matter, as the\n", - "# end-result is the same.\n", - "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)\n", - "# adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n", - "\n", - "# ArangoDB to RDF via Graph Name\n", - "g2, adb_mapping_2 = adbrdf.arangodb_graph_to_rdf(\"DataPGT\", Graph())\n", + "def graphs_are_identical(rdf_graph_a: Graph, rdf_graph_b: Graph) -> Graph:\n", + " assert rdf_graph_a and rdf_graph_b\n", + " return len(rdf_graph_a - rdf_graph_b) == 0 and len(rdf_graph_b - rdf_graph_a) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318, + "referenced_widgets": [ + "43792423dbaa49e4a4a19360091969b6", + "37a49fa1897849209764d68a6edd7008", + "23ca8452d91843d4bb367f79cb6fe21d", + "8f7a612cdc9646b1b3cdb3d2ef67a18e", + "52232abd9c904269997d568cc50c1fb6", + "0169a84a8bb74d7c8adc26dc9a701e2c", + "2ae4cd88a2684d4f876f62b0c0e1123d", + "632b440161b34a959a3fe82835f38f8d", + "b8ac74c5fc4b4eb499b6a196e187f2e5", + "aab83555df50479a88c3538e90b68fdc", + "409be936927846888c78a0cff15bf635", + "c81a9e5eb7bd497783e94cced16b94d5", + "71ddb22aed0446adbed06ade5f30a61d", + "7a3fc411802043c3b22e418a9d267bc1", + "f04b940f370643f68cca247a8cc5f952", + "f6bd0240ca004ff09b268afae04470de", + "387642d5ed1e408eba7516e6524c34f4", + "bb79559742424902a0693dc602c38f0e", + "fabcaecd1a0843afaf433bc2df936725", + "c1339ae8330144a0ba9a671411985389", + "8fb0eac7af4b4ba4836d5695deca6d32", + "31830517c9bc4134919413ff27dcdd00", + "e0515ddef3b54c19a674914c12730848", + "63bac0edac794f03b66da29a7d1d933d", + "274d1b85334c41e88282eae8d98df3dc", + "37768143af064eec94f846d5116caaa9", + "340471b0216d4e06852629be4c27f681", + "00ed07ecf31041ad84d32453fd43d0c1", + "13971de33d714affbdcbf4e17d7e6262", + "3e3c5ccb09e6420cbf4133930213227f", + "2417e2dc248f48f797adefe8cc228028", + "9983582ff7264e4cab10eed911e85f83", + "8903da96fe434ebc82efdb15a34a6edf", + "f0da88487b40424392885d47c76e5c8e", + "198e324979c14efdbb5990d057403a18", + "196c029b42e7468b8c1b2b82d93fb9d3", + "ab7f2a36f8354d82be9c6d8dc2940511", + "a69290567e9a4f809d50b4d02ad59ddc", + "b5a7dfdd273548ec9a75390e44f4f72d", + "6cba2fee3ce64170a5130d70695eec26", + "66bb8913622c403881eba0997ae6548f", + "1a60e18063d7443e9a7022a1bab0008f", + "98bee394813140c68b6cb5bfaba6a10c", + "51aa91d7110f4f90b69f83c790fb6b52", + "3b55e3255b46482fa0126d7df8fb6215", + "d0006980ed5642088dfb0f4766723316", + "de76ab207aa24b5187e8df6a244330f4", + "869184bdb77644ff9f5a7dfd62a20bc7", + "a37eb48a3cd74bc08b8880621c36c72f", + "ed62ee4dc04b400887a85c9186864cd8", + "9c80d8b208a44c6a9143dd3e9016d384", + "cc29a014f37240619b3c1e774e5a6b3c", + "12366995bad14594b1346c343bc10bdc", + "8310490b2e384846beb367a25d2cdf23", + "4ed9a5a4cce84f0aa47fe499455ac814", + "52e4fd6dcfae437b8f12e2d60e28318e", + "c685227c7509453ab0ba7c0a25ac3931", + "db1507b61b2447beb7226be27f091e63", + "de9df9c6f97d493da4a4236a4673e0c2", + "cd5583d33cb74601a6c1d822f5014846" + ] + }, + "id": "O94Dg2sVhTdT", + "outputId": "76636c9e-6627-4940-9bfe-00c7210491a5" + }, + "outputs": [], + "source": [ + "# PGT\n", + "adb_graph = adbrdf.rdf_to_arangodb_by_pgt(\n", + " \"DataPGT\",\n", + " get_graph(data, True),\n", + " overwrite_graph=True,\n", + ")\n", "\n", - "# ArangoDB to RDF via Collection Names\n", - "g3, adb_mapping_3 = adbrdf.arangodb_collections_to_rdf(\n", + "rdf_graph_new = adbrdf.arangodb_graph_to_rdf(\n", " \"DataPGT\",\n", - " Graph(),\n", - " v_cols={\"Album\", \"Band\", \"Class\", \"Property\", \"SoloArtist\", \"Song\"},\n", - " e_cols={\"artist\", \"member\", \"track\", \"type\", \"writer\"},\n", + " type(rdf_graph_original)()\n", ")\n", "\n", - "print(len(g2), len(adb_mapping_2))\n", - "print(len(g3), len(adb_mapping_3))\n", + "assert graphs_are_identical(rdf_graph_original, rdf_graph_new)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 168, + "referenced_widgets": [ + "5a2d4a845d274bb9bd81165ce410a444", + "f4e4607b23a54074b058c823703b1c53", + "dd28a24eec3d4539bfc5be068fdb4717", + "945453c61da944a087f84fefa5a7d421", + "2fc6b8341e474b62a6ee7daff89304d2", + "1b669b3013fc4ea4a9951b7db3771f3b", + "c17bf36fde304a7f841ec0f7657f1c4b", + "4c7729ca0129498a99a5c1a33117c21e", + "d84a5c85435840f0b1eee9c6f61f2bb7", + "efd59aa2a7474984af289f75f2c04b6a", + "dc0b77ee6c8b427da5b1969576a0fe55", + "ae8c90ad5c7d42c4b702373327e843a6", + "64ef034cf3e043359fa8a92e4c040f94", + "f7641041d47e4ac89ba017b5838dc440", + "87ee460987eb400ba53ef8a8ac0f83ec", + "7c27638420a14a1bb93c50865d63162a", + "7d618e6d56914751b3344f67135d89e6", + "f5d10a6ad6124b15829f3d9d64880c79", + "c5474e0551fd4080bb2c8b62cef9f3a1", + "91ae98fc5b2e451ba177255edf131409", + "b0d4d4d12b0449e791a866e9ec7dcbe1", + "16b2c93f497d4f62aa06a35052933fd1", + "7fe6e86bf2d942c293ee3fdd97dc863a", + "2741d4016cd94f18ac0d38c5f16de281" + ] + }, + "id": "Y51zcrJUfiL5", + "outputId": "30def815-e95f-4930-b6bc-9da664d748cf" + }, + "outputs": [], + "source": [ + "# RPT\n", + "adb_graph = adbrdf.rdf_to_arangodb_by_rpt(\n", + " \"DataRPT\",\n", + " get_graph(data, True),\n", + " overwrite_graph=True,\n", + ")\n", + "\n", + "rdf_graph_new = adbrdf.arangodb_graph_to_rdf(\n", + " \"DataRPT\",\n", + " type(rdf_graph_original)()\n", + ")\n", "\n", - "print('--------------------')\n", - "print(g2.serialize())\n", - "print('--------------------')\n", - "print(adb_mapping_2.serialize())\n", - "print('--------------------')" + "assert graphs_are_identical(rdf_graph_original, rdf_graph_new)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "uxp9AW7kQkM5" + "id": "_q34B2SQU22_" }, "source": [ - "#### Native" + "#### ArangoDB -> RDF -> ArangoDB" ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { - "id": "xoza5AvUVqWP" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 260, + "referenced_widgets": [ + "3e4cc848dfd54a94911c913f784b8731", + "6edb486ed2f24b35a80cac3b8c8cdc10", + "d3abeb9ccf0c42ed8de36317f3c01a47", + "6c3b09e3d1cf4dff8702402fc413cca0", + "daad61352bce433bb1d55a7120bf6782", + "6a1f66e50ad74367a52d2ae3f8f48f12", + "49da35b5b10c4203bbbfebce32b2a80f", + "27bc6eda9d0f4277bea6de3bc3decab4", + "933df8dd162440638f608b1ae6bd34da", + "17677c4248b1466baeb36a4a1725856a", + "aa13cae66ac64a8e9e365748bc5ff67f", + "54dc7c0868724390bf94e030c4287418", + "59d1812502754c6bb6701e3614ba2d94", + "fd39fc51745a4de086c6b7ca00c3f832", + "a0cae451b1c241beaf57344f2890c810", + "edc9a966c2634d9cbad3bac706115686", + "87b9a0caa3da42c3bf48158da7825856", + "8214ad5a0a52493aa93c363a5be2d221", + "3fcd605337d142a58bb2d7c656e6f509", + "e15e3352f99340069470a8d33416986f", + "787ab170a303420cb36ae0cfd43c9d63", + "8fbd43f55d92442f9fb057dfd38d732a", + "54b93b209d61498eb7d2da4bf24042ea", + "1b72c41a51244810befd264aad85f526", + "92bbd0f0d5a647abab9bff176af5a0fc", + "6f52f700d62c455f995dbe7e60ee899e", + "b056cebde1384bb49944a7322a01e32c", + "4069f0176c3c46c081f1c64fd5d50bdc", + "a6409ed1bff345a39fc3f4b562d8ebc9", + "43e9f9350be04c3897feef328fca07fa", + "58eaf8c7cacc4156acda80aa02e94761", + "fe56ad08950d44ae87c2f512092edd3d", + "4ea55c7b06794f9f81e86b202c5ba354", + "b57d0a227c1048d4961219ebf1d32530", + "c2bcea063da949c2b0bf0730c41b55ac", + "e7491ac5da904f1f8f06bc2643650839", + "a41722216871421697c751905d5433e4", + "8cb37d36be2e45c5adc9fe5a43cfd1e0", + "9d0664c32a6a4f859331c90df1d1b6b2", + "41d72b118f31481ca4318ca46b2915a6", + "54abea983db442579f8647cef03d6823", + "6436d487a2e94e41b51bcff155413b02", + "95b8c8d3d31b4a938974d0f5684ef529", + "af04648c3e4d414ab666e01ee3050311", + "d313578b458c4028ad993f1ac95c743a", + "5c748f9b979f4fe499a05558c49668ac", + "076b285dbfb94f72bb119aff2c4213a5", + "05bca57620264084a8d6706ed80eb206", + "28773c52acae44fabda65710a389c4c8", + "47f7cf67a9d9415da8fc00725bdca58d", + "61308ce8c6c2433fa2173b5707bd8598", + "a557d52c1e3247dd847c5d6870c0abf2", + "4bd5e014b1aa4b649a84f7ad4510f6d1", + "e2dab9c4ac714611a7d92f5d54e1a28b", + "3ebef63c1ed3413f8b2b84cba0b7a9d3", + "41b26d58b2094b629bdf38122d108a6f", + "3e869d0fc5be4643aea1cfa06f2d5281", + "14a0aa763fdf4d3b85e693a5393c71bb" + ] + }, + "id": "tCBsS5Q2jXrP", + "outputId": "f528702b-7f75-4be4-ed21-3fc339da1d57" }, + "outputs": [], "source": [ - "Native: ArangoDB Graphs that originate from an ArangoDB context. We'll be using the [ArangoDB Game Of Thrones Dataset](https://github.com/arangodb/example-datasets/tree/master/GameOfThrones).\n", + "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n", + "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n", "\n", - "Since we are dealing with a \"native\" ArangoDB Graph, we can rely on the `infer_type_from_adb_col` flag to indicate that `rdf:type` statements of the form (adb_doc rdf:type adb_col) should be inferred upon transferring ArangoDB Documents into RDF.\n", + "Datasets(db).load(\"OPEN_INTELLIGENCE_ANGOLA\")\n", "\n", - "We can also take advantage of the `include_adb_key_statements` flag to indicate that `adb:key` statements of the form (adb_doc adb:key adb_doc[\"key\"]) should be generated upon transferring ArangoDB Documents into RDF.\n", "\n", - "Note that enabling `infer_type_from_adb_col` `include_adb_key_statements` is only recommended if your ArangoDB graph is \"native\" to ArangoDB. That is, the ArangoDB graph does not originate from an RDF context.\n", + "def get_adb_graph_count(name: str):\n", + " global db\n", + " adb_graph = db.graph(name)\n", "\n", - "Finally, we set the `list_conversion_mode` flag to `collection` to indicate that JSON Lists within ArangoDB Documents should be converted into RDF Collections (other options include `container`, and `static`). " + " e_cols = {col[\"edge_collection\"] for col in adb_graph.edge_definitions()}\n", + "\n", + " v_count = 0\n", + " for v in db.graph(name).vertex_collections():\n", + " if v in e_cols:\n", + " continue\n", + "\n", + " v_count += adb_graph.vertex_collection(v).count()\n", + "\n", + " e_count = 0\n", + " for e_d in adb_graph.edge_definitions():\n", + " e_count += adb_graph.edge_collection(e_d[\"edge_collection\"]).count()\n", + "\n", + " return (v_count, e_count)\n", + "\n", + "original_v_count, original_e_count = get_adb_graph_count(\"OPEN_INTELLIGENCE_ANGOLA\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "yQ85OY7paqMM" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368, + "referenced_widgets": [ + "5a86d7d42ad74fb3bd471638a7e45df1", + "0ffed931b7e4411a882931ceb9d1b576", + "9aa19c471c064051a8ae962b1171ec24", + "cddf17d22f3447d199991a2ea1aa0564", + "8908115ec245468a9cf70d01f6349f90", + "25cc5340c82d4a60ad273cd6483dc48e", + "a683cf075e284cb5b2e57d47b9ad12e3", + "e1e3e373677b43dfa26d434d98a13ad9", + "13d371035c6e4441930620dee3c09ed0", + "14f1024bc2b349ad813ceab38b01f2dc", + "d359bbfd2725461baca4d2bd87e7c8d5", + "67e0fafdd9634aa69ac889342be69b51", + "af81fb0736e3444ab0e18760dd169e6a", + "803eef70784b4d5fb3cacd1abd7467cd", + "a551464a398b4bc9a3480717b6e65f2d", + "056a6e550f7f4f0eb763a4c9dec97a7b", + "c45d35ffd0884d7b9cf302de9a83f441", + "bcdf72b37a4d418ba3a7f97da7865629", + "526371c5ffa3455cb72359531671c385", + "b83f5e4968a44cc1bed752f63c23d45a", + "392bfe6ed1dd4327a9412a54cc4ab3d9", + "d96d5a9fba94419f93f6fc9f21e4ab10", + "8d909b7c90fa4caf8ea819dbed241e55", + "c4f776339f7248bc859bf49e61dbf37b", + "5501dd653f1647359616f7623579201c", + "910b3f36e0754872a132ee0076344338", + "f383807d016240daa720e0128fe960e5", + "d49a46b7410047008abd6117ba5cde00", + "d508449fbe214b4e92027329c253ba28", + "29ce0c02552f4a80aba63d003d1d13bc", + "3a132126a793411bac755f8981035861", + "9c61c2582e9e451488f01c22cc432d76", + "24c93b5825e04d07a0ac19b3054b2863", + "f73f626669b544d6a475cb528aada059", + "766d7599e584400f8f809227bb830fd1", + "ebc417617758470db76e670fe62473d5", + "d84cd061cf7440f287a581afe6f2e2a4", + "0ada0c44b2294efb8e7dc0307bb19fef", + "44c65c1407694742adc3f28968a2966c", + "0ff0e0b8a3424639aee19cb30ae03d45", + "8e7710a3934e4560afdc3ac42ae24da2", + "a0795fecba464f489c075ab0234d7580", + "77506a89557942298d13974e3cfb3283", + "77c43e3b70e244cfbdc97b60fabf75d4", + "9761b6144bff43e68155f661d777813d", + "5cb7eb9c39dc4ea6afbea5abce584d88", + "2966bbae3f8641f2a283bdcc1ed28033", + "f20624e96bcc46da8201c667f5affd34", + "1ad7fd19b2a14e138f58820275a3278c", + "1221b14e6fb44d9ab96518acd53bcb39", + "403355da7c0c452aa01188dec9f5197a", + "3fd821e5df744d36979ed8e4c21a64ab", + "3112012c724349f9a63467df384246bf", + "96c381aafc6c41de80080a60c6bd3433", + "41495fb3d42c4d88be5a5a883d3c3bc6", + "3be3d246e0424d1e94a921b94e1e0d9c", + "b59177f7df2e49bd8b499f81b70f6a36", + "df5c71b2fdf34623aa3b78871280b391", + "e68bc4e082df4493ba36e26684b62fa0", + "c3109b609f56431188eda471f4755219", + "1e28979e830648b1b3b4291d99eacb9b", + "8f0597ac3d684a55bb50b09036d76fd4", + "e8100f2fe07643b280aef789317c55e4", + "31df91153fe447bf8b4d5c2fbbded6e1", + "4dbed5e2c47f45f6b8617d13f14b743a", + "40d33cdc4f7d471e99668c2141a92e92", + "8fefa52620ae4ae798bb8698856404ae", + "9d28f57475024725b8ad8e8e66376c08", + "dd221f9487144800895f876010e45db1", + "f467e34102c644b3bb68bedb0fccf318", + "636701c209594d84bbbb6cd5dd620951", + "5e613d9d31f34b8883d8d0afae83cae7", + "37588cf454af45ad8a1c2c6ffb8c1dab", + "4ec0631a9829472fbf88cac4607a57f9" + ] + }, + "id": "K0ZN58bEjYFk", + "outputId": "4da5197e-fde3-4389-833d-e760aafed948" }, "outputs": [], "source": [ - "rdf_graph, adb_mapping = adbrdf.arangodb_graph_to_rdf(\"GameOfThrones\", rdf_graph=Graph(), list_conversion_mode=\"collection\", infer_type_from_adb_v_col=True, include_adb_key_statements=True)\n", - "print(rdf_graph.serialize())" + "# PGT\n", + "rdf_graph = adbrdf.arangodb_graph_to_rdf(\n", + " \"OPEN_INTELLIGENCE_ANGOLA\",\n", + " Graph(),\n", + " list_conversion_mode=\"serialize\",\n", + " dict_conversion_mode=\"serialize\",\n", + " include_adb_v_col_statements=True,\n", + " include_adb_v_key_statements=True,\n", + " include_adb_e_key_statements=True,\n", + ")\n", + "\n", + "\n", + "adb_graph = adbrdf.rdf_to_arangodb_by_pgt(\n", + " \"OPEN_INTELLIGENCE_ANGOLA\",\n", + " rdf_graph,\n", + " overwrite_graph=True\n", + ")\n", + "\n", + "property_v_count = adb_graph.vertex_collection(\"Property\").count()\n", + "\n", + "new_v_count, new_e_count = get_adb_graph_count(\"OPEN_INTELLIGENCE_ANGOLA\")\n", + "\n", + "assert original_v_count == new_v_count - property_v_count\n", + "assert original_e_count == new_e_count" ] } ], @@ -1582,15 +2984,14 @@ "44mc2EvIAzDy", "yRuJ3OIGE2Yr", "KnQifktFAxHx", - "7y81WHO8eG8_", "QfE_tKxneG9A", - "znQCjOwt7zBz", "0qry3Bcy-160", - "mRutdKii-Pk5", "0SWi4e3wIMtw", "9gBg-hDs77i7", - "UCQ9ppnUQa7e", - "uxp9AW7kQkM5" + "7mNWLGGTUipj", + "-FTakjQKUoWW", + "qddfFNtaUpoo", + "RGivoIz5Uv_-" ], "provenance": [] }, diff --git a/examples/notebook_output/ArangoRDF_output.ipynb b/examples/notebook_output/ArangoRDF_output.ipynb index d009101e..067c48c5 100644 --- a/examples/notebook_output/ArangoRDF_output.ipynb +++ b/examples/notebook_output/ArangoRDF_output.ipynb @@ -8,8 +8,8 @@ "source": [ "# **ArangoRDF**\n", "\n", - "\"rdf\"\n", - "\"rdf\"" + "\"rdf\"\n", + "\"rdf\"" ] }, { @@ -30,9 +30,9 @@ "outputs": [], "source": [ "%%capture\n", + "!pip install arango-datasets\n", "!pip install adb-cloud-connector\n", - "!pip install arango-rdf==0.1.0\n", - "!git clone https://github.com/ArangoDB-Community/ArangoRDF.git" + "!pip install arango-rdf" ] }, { @@ -43,14 +43,31 @@ }, "outputs": [], "source": [ - "from adb_cloud_connector import get_temp_credentials\n", - "from arango import ArangoClient\n", "import json\n", "\n", - "from rdflib import Graph, ConjunctiveGraph, URIRef, Literal, Namespace\n", - "from rdflib.namespace import RDFS, XSD\n", + "# ArangoDB\n", + "from arango import ArangoClient\n", + "from arango_rdf import ArangoRDF\n", + "from arango_datasets import Datasets\n", + "from adb_cloud_connector import get_temp_credentials\n", "\n", - "from arango_rdf import ArangoRDF" + "# RDF\n", + "from rdflib import Graph, ConjunctiveGraph, URIRef, Literal, Namespace\n", + "from rdflib.namespace import RDFS, XSD" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "9L0Q4cGTMGMG" + }, + "outputs": [], + "source": [ + "def get_graph(data: str, is_conjunctive_graph: bool = False) -> Graph:\n", + " g = ConjunctiveGraph() if is_conjunctive_graph else Graph()\n", + " g.parse(data=data, format='trig' if is_conjunctive_graph else 'ttl')\n", + " return g" ] }, { @@ -105,408 +122,408 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { - "id": "xiRxsYqY52BJ", - "outputId": "ceda6040-6c4a-4076-eda0-3d119fee3a94", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "xiRxsYqY52BJ", + "outputId": "adcd539c-dcf5-4e89-ab50-091406af9832" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ontology_alignment\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semantiko\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://vi.dbpedia.org/resource/Mạng_ngữ_nghĩa\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/topics yes\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs https://global.dbpedia.org/id/4jzLX\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://de.dbpedia.org/resource/Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://schema.org/birthPlace\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/commons Category:Semantic Web\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Con il termine web semantico, termine coniato dal suo ideatore, Tim Berners-Lee, si intende la trasformazione del World Wide Web in un ambiente dove i documenti pubblicati (pagine HTML, file, immagini, e così via) sono associati ad informazioni e dati (metadati) che ne specificano il contesto semantico in un formato adatto all'interrogazione e all'interpretazione (es. tramite motori di ricerca) e, più in generale, all'elaborazione automatica.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://sr.dbpedia.org/resource/Семантички_веб\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Biology\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Tacit_knowledge\n", "http://dbpedia.org/resource/Semantic_Web http://xmlns.com/foaf/0.1/depiction http://commons.wikimedia.org/wiki/Special:FilePath/RDF_example.svg\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Web Semantik (Web Bermakna) merujuk kepada teknik yang memungkinkan konten pada Web untuk dapat lebih dimengerti oleh komputer. Istilah Web Semantik itu sendiri dicetuskan oleh Tim Berners-Lee, penemu World Wide Web. Sekarang, prinsip Web Semantik disebut-sebut akan muncul pada Web 3.0, generasi ketiga dari World Wide Web. Bahkan, Web 3.0 itu sendiri sering disamakan dengan Web Semantik. Teknologi Web Semantik ini antara lain adalah RDF, OWL dan SPARQL.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_network\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://es.dbpedia.org/resource/Web_semántica\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Concept\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantically-Interlinked_Online_Communities\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Age104924103\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/W3C_XML_Schema\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/WikicatWebServices\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment セマンティック・ウェブ（英: semantic web）は W3C のティム・バーナーズ＝リーによって提唱された、ウェブページの意味を扱うことを可能とする標準やツール群の開発によってワールド・ワイド・ウェブの利便性を向上させるプロジェクト。セマンティック・ウェブの目的はウェブページの閲覧という行為に、データの交換の側面に加えて意味の疎通を付け加えることにある。 現在のワールド・ワイド・ウェブ上のコンテンツは主にHTMLで記述されている。HTMLでは文書構造を伝えることは可能だが、個々の単語の意味をはじめとする詳細な意味を伝えることはできない。これに対し、セマンティック・ウェブはXMLによって記述した文書にRDFやOWLを用いてタグを付け加える。この、データの意味を記述したタグが文書の含む意味を形式化し、コンピュータによる自動的な情報の収集や分析へのアプローチが可能となると期待されている。オントロジーを扱う階層まではW3Cにより標準化されているが、それ以上の階層の開発は難しいため、実現と標準化には長期間掛かると予想されている。また、既存のWebサイトに対するメタデータ付与の作業が必要であるため、Web全域への普及に関しても長期間掛かると予想されている。 近年では、Google検索のリッチスニペットなどの応用例が存在する。\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:R\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_web_service\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Ο Σημασιολογικός Ιστός (Web 3.0 ή Semantic Web στα αγγλικά) είναι μια επέκταση του σημερινού Ιστού, που θα φέρει δομή στο ουσιαστικό περιεχόμενο των ιστοσελίδων. Η λογική πίσω από αυτό είναι ότι η δημοσιευμένη πληροφορία θα περιέχει μεταδεδομένα, τα οποία θα είναι κοινά για όλους, θα μπορούν να «κατανοούνται» και από μηχανές, οι οποίες θα βοηθήσουν στην καλύτερη συλλογή και επεξεργασία τους. Στον επιχειρηματικό τομέα, θα υπάρχει καλύτερη οργάνωση των εταιριών, καλύτερες εμπειρίες για τους χρήστες στις διαδικτυακές αγορές και καλύτερος συντονισμός μεταξύ διαφορετικών εταιριών.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Branches_of_science\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://gl.dbpedia.org/resource/Web_semántica\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Data_model\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Altavista\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/n no\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Knowledge_engineering\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Semantiska webben är ett begrepp myntat av World Wide Web Consortiums (W3C) chef Tim Berners-Lee, som också är skapare av världswebben (WWW). Begreppet beskriver metoder och teknik för att möjliggöra för maskiner att förstå innebörden eller \"semantiken\" i informationen på webben. Den ursprungliga visionen var att tillgången på maskinläsbara metadata skulle möjliggöra för automatiska agenter och annan programvara att ansluta till internet på ett mer intelligent sätt.\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://th.dbpedia.org/resource/เว็บเชิงความหมาย\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment 语义网（英语：Semantic Web）是由万维网联盟的蒂姆·伯纳斯-李（Tim Berners-Lee）在1998年提出的一个概念，它的核心是：通过给万维网上的文档（如: HTML文档）添加能够被计算机所理解的语义（元数据），从而使整个互联网成为一个通用的信息交换媒介。语义万维网通过使用标准、置标语言和相关的处理工具来扩展万维网的能力。不过语意网概念实际上是基于很多已有技术的，也依赖于后来和text-and-markup与知识表现的综合。 \"语义\"网是由比现今成熟的网际搜索工具更加行之有效的、更加广泛意义的并且自动聚集和搜集信息的文档组成的。其最基本的元素就是。\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/File:RDF_example.svg\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Cognitive_science\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Clear\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Resource_(computer_science)\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ru.dbpedia.org/resource/Семантическая_паутина\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Human_science\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_computing\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://sr.dbpedia.org/resource/Семантички_веб\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label 시맨틱 웹\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Automated_reasoning_system\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/User_agent\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment El Web semàntic és un projecte que té com a objectiu crear un medi universal per a l'intercanvi d'informació significativa (semàntica), d'una forma comprensible per a les màquines, del contingut dels documents de la Web. Amb això es pretén ampliar la interoperabilitat dels sistemes informàtics i reduir la mediació dels operadors humans en els processos intel·ligents de flux d'informació. El pare de la idea, Tim Berners-Lee, creador del Web i fundador del W3C, proposa que la Web semàntica serveixi per a ampliar la capacitat de la World Wide Web mitjançant els , els llenguatges d'etiquetatge i altres eines de processament relacionades.\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label ويب دلالي\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_base\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Web_(journal)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Quote\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ora_Lassila\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://www.wikidata.org/entity/Q1731\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/d Q54837\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment La web semántica (del inglés semantic web) es un conjunto de actividades desarrolladas en el seno de World Wide Web Consortium con tendencia a la creación de tecnologías para publicar datos legibles por aplicaciones informáticas (máquinas en la terminología de la Web semántica).​ Se basa en la idea de añadir metadatos semánticos y ontológicos a la World Wide Web. Esas informaciones adicionales —que describen el contenido, el significado y la relación de los datos— se deben proporcionar de manera formal, para que así sea posible evaluarlas automáticamente por máquinas de procesamiento. El objetivo es mejorar Internet ampliando la interoperabilidad entre los sistemas informáticos usando \"agentes inteligentes\". Agentes inteligentes son programas en las computadoras que buscan información sin \n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Hyperlink\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/Person\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ta.dbpedia.org/resource/வலை_3.0\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Property104916342\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment セマンティック・ウェブ（英: semantic web）は W3C のティム・バーナーズ＝リーによって提唱された、ウェブページの意味を扱うことを可能とする標準やツール群の開発によってワールド・ワイド・ウェブの利便性を向上させるプロジェクト。セマンティック・ウェブの目的はウェブページの閲覧という行為に、データの交換の側面に加えて意味の疎通を付け加えることにある。 現在のワールド・ワイド・ウェブ上のコンテンツは主にHTMLで記述されている。HTMLでは文書構造を伝えることは可能だが、個々の単語の意味をはじめとする詳細な意味を伝えることはできない。これに対し、セマンティック・ウェブはXMLによって記述した文書にRDFやOWLを用いてタグを付け加える。この、データの意味を記述したタグが文書の含む意味を形式化し、コンピュータによる自動的な情報の収集や分析へのアプローチが可能となると期待されている。オントロジーを扱う階層まではW3Cにより標準化されているが、それ以上の階層の開発は難しいため、実現と標準化には長期間掛かると予想されている。また、既存のWebサイトに対するメタデータ付与の作業が必要であるため、Web全域への普及に関しても長期間掛かると予想されている。 近年では、Google検索のリッチスニペットなどの応用例が存在する。\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/RDFS\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Social_Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Семантична павутина (англ. Semantic web) — нова концепція розвитку Всесвітньої павутини і мережі Інтернет, яка створена і впроваджується Консорціумом Всесвітньої павутини (англ. World Wide Web Consortium, W3C). Інші назви — семантичний веб, семантична мережа. Хоча поняття семантична мережа, яке виникло раніше, породило поняття семантична павутина, їх слід відокремлювати. Термін вперше запровадив Тім Бернерс-Лі в травні 2001 року в журналі «Scientific American»\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/YagoPermanentlyLocatedEntity\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantika Reto\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Семанти́ческая паути́на (от англ. semantic web) — общедоступная глобальная семантическая сеть, формируемая на базе Всемирной паутины путём стандартизации представления информации в виде, пригодном для машинной обработки.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label ويب دلالي\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Cite_journal\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Entity–attribute–value_model\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Semantika Reto (Angle:Semantic web) estas nova koncepto pri evoluo de Interreto, kiun alprenis kaj disvastigas Konsorcio de la Tutmonda Teksaĵo. Iufoje oni ĝin nomas Semantika Teksaĵo, Semantika Plekto.Tiu koncepto permesas publikigi, konsulti, kaj speciale aŭtomatigi la traktadon de sciadoj strukturitaj.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Defeasible_reasoning\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Microdata_(HTML)\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Further\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Mastodon_(software)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Artificial_intelligence\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.wikidata.org/entity/Q1731\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Семанти́ческая паути́на (от англ. semantic web) — общедоступная глобальная семантическая сеть, формируемая на базе Всемирной паутины путём стандартизации представления информации в виде, пригодном для машинной обработки. В обычной Всемирной паутине, основанной на HTML-страницах, информация заложена в тексте страниц и предназначена для чтения и понимания человеком. Семантическая паутина состоит из машинно-читаемых элементов — узлов семантической сети, с опорой на онтологии. Благодаря этому программы-клиенты получают возможность непосредственно получать из интернета утверждения вида «предмет — вид взаимосвязи — другой предмет» и вычислять по ним логические заключения. Семантическая паутина работает параллельно с обычной Всемирной паутиной и на её основе, используя протокол HTTP и идентификаторы ресурсов URI. Название «Семантическая паутина» было впервые введено Тимом Бернерсом-Ли (изобретателем Всемирной паутины) в сентябре 1998 года, и называется им «следующим шагом в развитии Всемирной паутины». Позже в своём блоге он предложил в качестве синонима термин «гигантский глобальный граф» (англ. giant global graph, GGG, по аналогии с WWW). Концепция семантической паутины была принята и продвигается консорциумом Всемирной паутины.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/E-learning\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_browser\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Short_description\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semàntic\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Multi-agent_systems\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://schema.org/Person\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/API\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Internet_of_things\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://d-nb.info/gnd/4688372-1\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/infocom yes\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Service100577525\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Digital_humanities\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Cory_Doctorow\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Machine-readable_data\n", + "http://dbpedia.org/resource/Semantic_Web 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http://pt.dbpedia.org/resource/Web_semântica\n", + "http://dbpedia.org/resource/Semantic_Web http://purl.org/linguistics/gold/hypernym http://dbpedia.org/resource/Extension\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://bg.dbpedia.org/resource/Семантична_мрежа\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Metacrap\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Amazon.com\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Timo_Honkela\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Scholia\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Σημασιολογικός Ιστός\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Giant_Global_Graph\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web3\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Probabilistic_logic\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Computational_semantics\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Semantiska webben är ett begrepp myntat av World Wide Web Consortiums (W3C) chef Tim Berners-Lee, som också är skapare av världswebben (WWW). Begreppet beskriver metoder och teknik för att möjliggöra för maskiner att förstå innebörden eller \"semantiken\" i informationen på webben. Den ursprungliga visionen var att tillgången på maskinläsbara metadata skulle möjliggöra för automatiska agenter och annan programvara att ansluta till internet på ett mer intelligent sätt. Även om termen \"semantiska webben\" inte är formellt definierad, så används den för att beskriva den modell och de tekniker som föreslagits av W3C. Dessa tekniker inkluderar Resource Description Framework (RDF), olika format för datautbyte (till exempel RDF / XML, JSON-LD, Notation 3, Turtle, N-Tripplar och notationer som RDF Schema och Web Ontology Language (OWL), som alla syftar till att ge en formell beskrivning av koncept, termer och relationer inom ett givet kunskapsområde. Många av de tekniker som föreslagits av W3C finns redan och används i olika projekt. Den semantiska webben som en global vision har dock inte ännu realiserats, och dess kritiker har ifrågasatt om tillvägagångssättet är möjligt. Dessutom har andra tekniker med liknande mål, till exempel mikroformat, utvecklats, vilka inte alltid beskrivs som \"den semantiska webben\".\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/HTTP\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/RDFS\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/b Semantic Web\n", + "http://dbpedia.org/resource/Semantic_Web http://xmlns.com/foaf/0.1/depiction http://commons.wikimedia.org/wiki/Special:FilePath/RDF_example_extended.svg\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Linked_data\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Emerging_technologies\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Σημασιολογικός Ιστός\n", - 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"http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageInterLanguageLink http://commons.dbpedia.org/resource/File:Aaron_Swartz_s_A_Programmable_Web_An_Unfinished_Work.pdf\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Linked_Data\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://id.dbpedia.org/resource/Web_semantik\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_translation\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/WikicatInternetAges\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Act100030358\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Folksonomy\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/itemtype%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Cite_journal\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_integration\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Metadata\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Web semantikoa (semantic web) World Wide Web Consortiumen muinean garatutako jarduera multzo bat da, aplikazio informatikoek datu irakurgarriak argitaratzeko teknologia bermatzen duena. World Wide Webari metadatu ontologiko eta semantikoak gehitzeko ideian oinarritzen da. Helburua interneta hobetzea da sistema informatikoen elkargarritasuna handituz, eragile adimenduak erabiliz. Eragile adimenduak informazioa era automatikoan eta gizakien laguntzarik gabe bilatzen duten programa informatikoak dira. Ideia honen aitzindaria, Tim Berners-Lee, hasieratik saiatu zen informazio semantikoa sartzen haren sorkuntzan (World Wide Web), baina arazo desberdinengatik ez zuen lortu. Arrazoi horrengatik landu zuen semantika.\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://gl.dbpedia.org/resource/Web_semántica\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Biology\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Data_sharing\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Amazon.com\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Семанти́ческая паути́на (от англ. semantic web) — общедоступная глобальная семантическая сеть, формируемая на базе Всемирной паутины путём стандартизации представления информации в виде, пригодном для машинной обработки.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ora_Lassila\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Free_University_of_Berlin\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_engineering\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Citation_needed\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ontology_alignment\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/JSON-LD\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Authority_control\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/N-Triples\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_Ontology_Language\n", + "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Internet_ages\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Web_Rule_Language\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Metadata\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Div_col_end\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Linked_Open_Data\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/SPARQL\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web sémantique\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Quote\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Het semantisch web verschaft een framework waarmee gegevens kunnen worden gedeeld en hergebruikt. Het is een samenwerking onder leiding van het internationale orgaan voor internetstandaarden, het World Wide Web Consortium W3C. Het semantisch web is een uitbreiding van het internet, om de uitwisseling van gegevens tussen de deelnemers, tussen alle nodes, efficiënter te laten verlopen. Open data zijn de vrij beschikbare informatie, die door het semantisch web wordt gebruikt.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ca.dbpedia.org/resource/Web_semàntic\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_matching\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_search_engine\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Computable_knowledge\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_crawler\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/W3C_XML_Schema\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/ontology/MusicGenre\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_social_network\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://is.dbpedia.org/resource/Merkingarvefur\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Information_overload\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment A Web semântica é uma extensão da World Wide Web que permite aos computadores e humanos trabalharem em cooperação. Ela interliga significados de palavras e, neste âmbito, tem como finalidade conseguir atribuir um significado (sentido) aos conteúdos publicados na Internet de modo que seja compreensível tanto pelo humano como pelo computador. A ideia da Web semântica surgiu em 2001, quando Tim Berners-Lee, James Hendler e Ora Lassila publicaram um artigo na revista Scientific American, intitulado: “Web Semântica: um novo formato de conteúdo para a Web que tem significado para computadores vai iniciar uma revolução de novas possibilidades” (The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities).\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Medical_terminology\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://archive.org/details/isbn_9780470396797\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Fuzzy_logic\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Digital_humanities\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/API\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ta.dbpedia.org/resource/வலை_3.0\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://schema.org/birthPlace\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://de.dbpedia.org/resource/Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/FOAF_(software)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://example.org/semantic-web/%22%3ESemantic\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract The Semantic Web, sometimes known as Web 3.0 (not to be confused with Web3), is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable. To enable the encoding of semantics with the data, technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used. These technologies are used to formally represent metadata. For example, ontology can describe concepts, relationships between entities, and categories of things. These embedded semantics offer significant advantages such as reasoning over data and operating with heterogeneous data sources. These standards promote common data formats and exchange protocols on the Web, fundamentally the RDF. According to the W3C, \"The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.\" The Semantic Web is therefore regarded as an integrator across different content and information applications and systems. The term was coined by Tim Berners-Lee for a web of data (or data web) that can be processed by machines—that is, one in which much of the meaning is machine-readable. While its critics have questioned its feasibility, proponents argue that applications in library and information science, industry, biology and human sciences research have already proven the validity of the original concept. Berners-Lee originally expressed his vision of the Semantic Web in 1999 as follows: I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A \"Semantic Web\", which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The \"intelligent agents\" people have touted for ages will finally materialize. The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web. In 2006, Berners-Lee and colleagues stated that: \"This simple idea…remains largely unrealized\".In 2013, more than four million Web domains (out of roughly 250 million total) contained Semantic Web markup.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Internet_censorship\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://el.dbpedia.org/resource/Σημασιολογικός_Ιστός\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Intelligent_text_analysis\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Principle_of_explosion\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_technology\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract El Web semàntic és un projecte que té com a objectiu crear un medi universal per a l'intercanvi d'informació significativa (semàntica), d'una forma comprensible per a les màquines, del contingut dels documents de la Web. Amb això es pretén ampliar la interoperabilitat dels sistemes informàtics i reduir la mediació dels operadors humans en els processos intel·ligents de flux d'informació. El pare de la idea, Tim Berners-Lee, creador del Web i fundador del W3C, proposa que la Web semàntica serveixi per a ampliar la capacitat de la World Wide Web mitjançant els , els llenguatges d'etiquetatge i altres eines de processament relacionades.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment 시맨틱 웹(Semantic Web)은 '의미론적인 웹'이라는 뜻으로,현재의 인터넷과 같은 분산환경에서 리소스(웹 문서, 각종 파일, 서비스 등)에 대한 정보와 자원 사이의 관계-의미 정보(Semanteme)를 기계(컴퓨터)가 처리할 수 있는 온톨로지 형태로 표현하고, 이를 자동화된 기계(컴퓨터)가 처리하도록 하는 프레임워크이자 기술이다. 웹의 창시자인 팀 버너스리가 1998년 제안했고 현재 W3C에 의해 표준화 작업이 진행 중이다.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://lt.dbpedia.org/resource/Semantinis_tinklas\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semántica\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://es.dbpedia.org/resource/Web_semántica\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_2.0\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Sémantický web\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Relational_database\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Institut_national_de_recherche_en_informatique_et_en_automatique\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/birthPlace%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/OpenAlex\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/XHTML\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://rdf.freebase.com/ns/m.0bbv9w2\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Scalable_vector_graphics\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/Place%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment The Semantic Web, sometimes known as Web 3.0 (not to be confused with Web3), is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable. Berners-Lee originally expressed his vision of the Semantic Web in 1999 as follows:\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Information_science\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://schema.org/Person%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Abstraction100002137\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Privacy\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://mk.dbpedia.org/resource/Семантичка_пајажина\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/birthPlace%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Семантическая паутина\n", - "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Web_services\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://it.dbpedia.org/resource/Web_semantico\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://nl.dbpedia.org/resource/Semantisch_web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Paraconsistent_logic\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Hypertext_Markup_Language\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_engineering\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantic Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Internet_censorship\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Code\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Authority_control\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Het semantisch web verschaft een framework waarmee gegevens kunnen worden gedeeld en hergebruikt. Het is een samenwerking onder leiding van het internationale orgaan voor internetstandaarden, het World Wide Web Consortium W3C. Het semantisch web is een uitbreiding van het internet, om de uitwisseling van gegevens tussen de deelnemers, tussen alle nodes, efficiënter te laten verlopen. Open data zijn de vrij beschikbare informatie, die door het semantisch web wordt gebruikt.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Internet\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Geospatial_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ur.dbpedia.org/resource/رابط_معنائی\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Semantics\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://yago-knowledge.org/resource/Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_technology\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Allan_M._Collins\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.wikidata.org/entity/Q1731\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Usability\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Tag_(metadata)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_HTML\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://zh.dbpedia.org/resource/语义网\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract A Web semântica é uma extensão da World Wide Web que permite aos computadores e humanos trabalharem em cooperação. Ela interliga significados de palavras e, neste âmbito, tem como finalidade conseguir atribuir um significado (sentido) aos conteúdos publicados na Internet de modo que seja compreensível tanto pelo humano como pelo computador. A ideia da Web semântica surgiu em 2001, quando Tim Berners-Lee, James Hendler e Ora Lassila publicaram um artigo na revista Scientific American, intitulado: “Web Semântica: um novo formato de conteúdo para a Web que tem significado para computadores vai iniciar uma revolução de novas possibilidades” (The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities). A web semântica é a relação de interatividade do homem e o computador, onde um depende do outro para a conclusão de tarefas, assim ocorrendo a acepção de entendimento do comando estipulado do ser humano à interface operacional da máquina, dando um significado de ordem a ser executada. Sendo desta forma uma espécie de escrita de entendimento tanto para o computador e para usuário, por meio de organização das informações contidas na máquina, através do seu perfil de suas últimas pesquisas realizadas, originando um repositório de dados ligados, como se fosse um verbete de enciclopédia. Podemos fazer uma ligação da web semântica com a Ciência da Informação, pelo fato de possuir a ideia estrutural de arranjo sistêmico, agrupando um conjunto de coleta de análise, classificação, armazenamento, disseminação e recuperação de informação como se fosse uma enciclopédia. Esta ligação, entre a Ciência da Informação e a Web Semântica, pode ser encontrada no projeto da Rede Universal de Documentação de Paul Otlet, que assenta nos mesmos ideais. O objetivo principal da web semântica não é treinar as máquinas para que se comportem como pessoas, mas sim desenvolver tecnologias e linguagens que tornem a informação legível para as máquinas. A finalidade passa pelo desenvolvimento de um modelo tecnológico que permita a partilha global de conhecimento assistido por máquinas. A integração das linguagens ou tecnologias eXtensible Markup Language (XML), Resource Description Framework (RDF), arquiteturas de metadados, ontologias, agentes computacionais, entre outras, favorecerá o aparecimento de serviços Web que garantam a interoperabilidade e cooperação.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/GRDDL\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Unicode\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Frank_van_Harmelen\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_Navigator\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Ο Σημασιολογικός Ιστός (Web 3.0 ή Semantic Web στα αγγλικά) είναι μια επέκταση του σημερινού Ιστού, που θα φέρει δομή στο ουσιαστικό περιεχόμενο των ιστοσελίδων. Η λογική πίσω από αυτό είναι ότι η δημοσιευμένη πληροφορία θα περιέχει μεταδεδομένα, τα οποία θα είναι κοινά για όλους, θα μπορούν να «κατανοούνται» και από μηχανές, οι οποίες θα βοηθήσουν στην καλύτερη συλλογή και επεξεργασία τους. Ο Σημασιολογικός Ιστός βασίζεται σε τεχνολογίες που ήδη υπάρχουν (URI και XML) αλλά και σε νέες τεχνολογίες (RDF, RDFS, OWL, κα.), οι οποίες αναπτύσσονται με την βοήθεια της κοινότητας. Δεδομένου ότι ο νέος Ιστός σκοπεύει να είναι μια μεγάλη βάση όπου δεδομένα από διαφορετικά πεδία θα συνδέονται μεταξύ τους, αναμένεται να παίξει μεγάλο ρόλο στη ζωή μας. Μερικά από τα πεδία στα οποία αναμένεται να έχει την μεγαλύτερη επίδραση είναι στην υγεία, στην παιδεία και στις επιχειρήσεις. Υπάρχουν ήδη πολλές προσπάθειες από εταιρίες, ερευνητές και μη κερδοσκοπικές οργανώσεις για να παραγάγουν πρότυπα οντολογιών, κυρίως για τα παραπάνω πεδία, για να υπάρχουν κοινές γλώσσες και περισσότερα δεδομένα τα οποία να μπορούν να συνδυαστούν για καλύτερα αποτελέσματα. Στην υγεία, γίνεται προσπάθεια για τη δημιουργία ενοποιημένων γλωσσών ιατρικής ορολογίας και υπηρεσίες που θα βοηθάνε το ιατρικό προσωπικό και θα κατευθύνουν τους καταναλωτές σε αξιόπιστες πληροφορίες υγείας σχετικά με την κατάστασή τους. Στην εκπαίδευση, ο Σημασιολογικός Ιστός θα συμβάλει σημαντικά στην μάθηση κυρίως στον τρόπο αναζήτησης πληροφοριών, στην οργάνωση των αποτελεσμάτων και στη δημιουργία ενός προγράμματος μάθησης ειδικό για το καθένα. Στον επιχειρηματικό τομέα, θα υπάρχει καλύτερη οργάνωση των εταιριών, καλύτερες εμπειρίες για τους χρήστες στις διαδικτυακές αγορές και καλύτερος συντονισμός μεταξύ διαφορετικών εταιριών. Στην καθημερινότητά θα υπάρχουν επιδράσεις του Web 3.0 στα κοινωνικά δίκτυα και εικονικές κοινότητες. Θα υπάρχουν εφαρμογές, οι οποίες θα δίνουν περισσότερες, πιο έμπιστες, πληροφορίες και θα διευκολύνουν σημαντικά τις διαδικτυακές δραστηριότητες.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Hypertext_Markup_Language\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/n no\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_query\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Sémantický web se má stát novým evolučním stupněm stávajícího webu. Jedná se o web, kde jsou informace strukturovány a uloženy podle standardizovaných pravidel, což usnadňuje jejich vyhledání a zpracování. Staví zejména na Resource Description Framework (RDF) a (OWL). Na specifikacích pro sémantický web pracuje World Wide Web Consortium (W3C). Myšlenku sémantického, neboli významového webu poprvé vyslovil Tim Berners-Lee v roce 2001, kdy upozornil na skutečnost, že současný web je pouze změť webových stránek, která neustále roste a ve které je stále složitější nalézt relevantní informace. Sémantický web je podle něj rozšířením současného webu, v němž informace mají přidělen dobře definovaný význam lépe umožňující počítačům a lidem spolupracovat. Sémantický web představuje reprezentaci dat na WWW. Je založen na technologii Resource Description Framework (RDF), která integruje širokou škálu aplikací využívajících syntaktický zápis v XML a identifikátory URI pro pojmenovávání. Znamená to tedy, že data prezentovaná na Internetu by měla mít přesně definovaný význam a dovolovat do značné míry automatizované (strojové) zpracování, které by se mělo realizovat pomocí softwarových agentů. Základním krokem k vytvoření sémantického webu je konceptualizace dat dostupných na Internetu, jejíž klíčovým nástrojem jsou ontologie, aneb formalizované reprezentace znalostí určené k jejich sdílení a znovupoužití. Sémantický web je dále založen na standardizovaném popisu webových zdrojů (vše, dosažitelné pomocí WWW, tedy textové dokumenty, obrázky, videosekvence, zvukové soubory apod.). Každý zdroj by byl vybaven stejnými charakteristikami údaji (autor, typ zdroje, klíčová slova atd.), což by umožnilo uživatelům Internetu pracovat se sítí WWW jako s relační databází a dotazovat se na její obsah prostřednictvím jazyků podobných SQL. Důraz by se kladl na vysokou přesnost a relevanci odpovědi na vyhledávací dotaz. Technologickým základem sémantického webu by se měl stát standard RDF (Resource Description Framework) - obecný rámec pro popis, výměnu a znovupoužití metadat. Poskytuje jednoduchý model pro popis zdrojů, který není závislý na konkrétní implementaci. Datový model RDF umožní specifikovat trojice (zdroj, vlastnost, hodnota vlastnosti). Tímto se jedná mimo jiné o přiřazení sémantiky webovým zdrojům, což je pro sémantický web klíčové. Sémantický web propaguje Tim Berners-Lee již řadu let, přesto se dosud nedočkal výrazného rozmachu, pravděpodobně protože je oproti stávajícímu webu příliš komplikovaný. Proto vznikají jednodušší způsoby, jak do stávajícího webu snadno přidat sémantickou informaci: Mikroformáty a RDFa.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Inference\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Communication100033020\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Семантична павутина (англ. Semantic web) — нова концепція розвитку Всесвітньої павутини і мережі Інтернет, яка створена і впроваджується Консорціумом Всесвітньої павутини (англ. World Wide Web Consortium, W3C). Інші назви — семантичний веб, семантична мережа. Хоча поняття семантична мережа, яке виникло раніше, породило поняття семантична павутина, їх слід відокремлювати. Термін вперше запровадив Тім Бернерс-Лі в травні 2001 року в журналі «Scientific American»\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://www.w3.org/2002/07/owl%23equivalentClass%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://el.dbpedia.org/resource/Σημασιολογικός_Ιστός\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://uk.dbpedia.org/resource/Семантична_павутина\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Web Semantik (Web Bermakna) merujuk kepada teknik yang memungkinkan konten pada Web untuk dapat lebih dimengerti oleh komputer. Istilah Web Semantik itu sendiri dicetuskan oleh Tim Berners-Lee, penemu World Wide Web. Sekarang, prinsip Web Semantik disebut-sebut akan muncul pada Web 3.0, generasi ketiga dari World Wide Web. Bahkan, Web 3.0 itu sendiri sering disamakan dengan Web Semantik. Teknologi Web Semantik ini antara lain adalah RDF, OWL dan SPARQL.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/name%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantics\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Tim_O'Reilly\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/DBpedia\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Multi-agent_systems\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Extensibility\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Information_overload\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Sémantický web se má stát novým evolučním stupněm stávajícího webu. Jedná se o web, kde jsou informace strukturovány a uloženy podle standardizovaných pravidel, což usnadňuje jejich vyhledání a zpracování. Staví zejména na Resource Description Framework (RDF) a (OWL). Na specifikacích pro sémantický web pracuje World Wide Web Consortium (W3C).\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Abstraction100002137\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Anchor\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/Person%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/HTML_element\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Buzzword106608277\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://pt.dbpedia.org/resource/Web_semântica\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://data.wordlift.io/wl0216/entity/semantic_web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Concept\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Trust_service\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Semantics\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ar.dbpedia.org/resource/ويب_دلالي\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semantico\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/MIT_Press\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Computational_semantics\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Het semantisch web verschaft een framework waarmee gegevens kunnen worden gedeeld en hergebruikt. Het is een samenwerking onder leiding van het internationale orgaan voor internetstandaarden, het World Wide Web Consortium W3C. Het semantisch web is een uitbreiding van het internet, om de uitwisseling van gegevens tussen de deelnemers, tussen alle nodes, efficiënter te laten verlopen. Open data zijn de vrij beschikbare informatie, die door het semantisch web wordt gebruikt.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Nonsense106607339\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ko.dbpedia.org/resource/시맨틱_웹\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semantik\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/v no\n", + "http://dbpedia.org/resource/Semantic_Web http://xmlns.com/foaf/0.1/depiction http://commons.wikimedia.org/wiki/Special:FilePath/Semantic_web_stack.svg\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/W3C\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_network\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Scholia\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment 시맨틱 웹(Semantic Web)은 '의미론적인 웹'이라는 뜻으로,현재의 인터넷과 같은 분산환경에서 리소스(웹 문서, 각종 파일, 서비스 등)에 대한 정보와 자원 사이의 관계-의미 정보(Semanteme)를 기계(컴퓨터)가 처리할 수 있는 온톨로지 형태로 표현하고, 이를 자동화된 기계(컴퓨터)가 처리하도록 하는 프레임워크이자 기술이다. 웹의 창시자인 팀 버너스리가 1998년 제안했고 현재 W3C에 의해 표준화 작업이 진행 중이다.\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://eo.dbpedia.org/resource/Semantika_Reto\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Entity–relationship_model\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ontology_(information_science)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Computable_knowledge\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:'%22\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://fr.dbpedia.org/resource/Web_sémantique\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Reflist\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment إن ورود المعلومات على الإنترنت يزداد بشكل كبير، فقد أصبح الإنترنت مكانًا للتعبير عن الأفكار، سرد القصص، إنشاء المدونات ومشاركة الفيديوهات والصور والملفات الصوتية وما إلى ذلك. وهو ما جعل كمّ المعلومات المتوفرة للفرد الواحد أكبر بكثير مما يُمكن له أن يستفيد منه. تعريض العقل البشري لهذا الكم الهائل من المعلومات من شأنه أن يتسبب فيما يُمكن أن نصفه بوصف «الضياع في فضاء المعلومات»، وذلك راجع إلى بقاء المعلومات المفيدة بعيدة المنال بسبب تراكم الكثير من المعلومات غير المفيدة وغير المرتبطة بالموضوع المراد البحث عنه من قبل المستخدم. لحسن الحظ، مثلما يزداد ورود المعلومات، تزداد مقدرات معالجة المعلومات اوتوماتيكياً، لذا يوجد إمكانيات كبيرة للاستفادة من مقدرات الأتمتة هذه بهدف استخراج المعلومات والخدمات من فيضان الويب والمرتبطة بالمستخدم، وتوصيلها إليه عن طريق واجهة مستخدم معيارية (Standardized User In\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Aaron_Swartz\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract 语义网（英语：Semantic Web）是由万维网联盟的蒂姆·伯纳斯-李（Tim Berners-Lee）在1998年提出的一个概念，它的核心是：通过给万维网上的文档（如: HTML文档）添加能够被计算机所理解的语义（元数据），从而使整个互联网成为一个通用的信息交换媒介。语义万维网通过使用标准、置标语言和相关的处理工具来扩展万维网的能力。不过语意网概念实际上是基于很多已有技术的，也依赖于后来和text-and-markup与知识表现的综合。 \"语义\"网是由比现今成熟的网际搜索工具更加行之有效的、更加广泛意义的并且自动聚集和搜集信息的文档组成的。其最基本的元素就是。\n", - "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Le Web sémantique, ou toile sémantique, est une extension du Web standardisée par le World Wide Web Consortium (W3C). Ces standards encouragent l'utilisation de formats de données et de protocoles d'échange normés sur le Web, en s'appuyant sur le modèle Resource Description Framework (RDF). Le Web sémantique est par certains qualifié de Web 3.0.\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://he.dbpedia.org/resource/רשת_סמנטית\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Das Semantic Web erweitert das Web, um Daten zwischen Rechnern einfacher austauschbar und für sie einfacher verwertbar zu machen; so kann beispielsweise das Wort „Bremen“ in einem Webdokument um die Information ergänzt werden, ob hier der Begriff des Schiffs-, Familien- oder Stadtnamens gemeint ist. Diese zusätzlichen Informationen explizieren die sonst nur unstrukturiert vorkommenden Daten. Zur Realisierung dienen Standards zur Veröffentlichung und Nutzung maschinenlesbarer Daten (insbesondere RDF).Während Menschen solche Informationen aus dem gegebenen Kontext schließen können (aus dem Gesamttext, über die Art der Publikation oder der Rubrik in selbiger, Bilder etc.) und derartige Verknüpfungen unbewusst aufbauen, muss Maschinen dieser Kontext erst beigebracht werden; hierzu werden die Inhalte mit weiterführenden Informationen verknüpft. Das Semantic Web beschreibt dazu konzeptionell einen „Giant Global Graph“ (engl. ‚gigantischer globaler Graph‘). Dabei werden sämtliche Dinge von Interesse identifiziert und mit einer eindeutigen Adresse versehen als Knoten angelegt, die wiederum durch Kanten (ebenfalls jeweils eindeutig benannt) miteinander verbunden sind. Einzelne Dokumente im Web beschreiben dann eine Reihe von Kanten, und die Gesamtheit all dieser Kanten entspricht dem globalen Graphen.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Emerging_technologies\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Giant_Global_Graph\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://bg.dbpedia.org/resource/Семантична_мрежа\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageLength 48380\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label 语义网\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://rdf.freebase.com/ns/m.0bbv9w2\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Tacit_knowledge\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/Person\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/ActivityPub\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Message106598915\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.w3.org/1999/02/22-rdf-syntax-ns%23type%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Семантична павутина (англ. Semantic web) — нова концепція розвитку Всесвітньої павутини і мережі Інтернет, яка створена і впроваджується Консорціумом Всесвітньої павутини (англ. World Wide Web Consortium, W3C). Інші назви — семантичний веб, семантична мережа. Хоча поняття семантична мережа, яке виникло раніше, породило поняття семантична павутина, їх слід відокремлювати. Концепція полягає у впровадженні спільних, стандартних форматів даних у Мережі. Для заохочення впровадження семантичного форматування сторінок, пропонується змінювати структуру вже існуючих, не структурованих чи частково-структурованих сторінок у «мережу даних». Створення семантичної Мережі полягає у застосуванні середовища опису ресурсів (RDF). Термін вперше запровадив Тім Бернерс-Лі в травні 2001 року в журналі «Scientific American»\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract El Web semàntic és un projecte que té com a objectiu crear un medi universal per a l'intercanvi d'informació significativa (semàntica), d'una forma comprensible per a les màquines, del contingut dels documents de la Web. Amb això es pretén ampliar la interoperabilitat dels sistemes informàtics i reduir la mediació dels operadors humans en els processos intel·ligents de flux d'informació. El pare de la idea, Tim Berners-Lee, creador del Web i fundador del W3C, proposa que la Web semàntica serveixi per a ampliar la capacitat de la World Wide Web mitjançant els , els llenguatges d'etiquetatge i altres eines de processament relacionades.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract La web semántica (del inglés semantic web) es un conjunto de actividades desarrolladas en el seno de World Wide Web Consortium con tendencia a la creación de tecnologías para publicar datos legibles por aplicaciones informáticas (máquinas en la terminología de la Web semántica).​ Se basa en la idea de añadir metadatos semánticos y ontológicos a la World Wide Web. Esas informaciones adicionales —que describen el contenido, el significado y la relación de los datos— se deben proporcionar de manera formal, para que así sea posible evaluarlas automáticamente por máquinas de procesamiento. El objetivo es mejorar Internet ampliando la interoperabilidad entre los sistemas informáticos usando \"agentes inteligentes\". Agentes inteligentes son programas en las computadoras que buscan información sin operadores humanos. El precursor de la idea, Tim Berners-Lee, intentó desde el principio incluir información semántica en su creación, la World Wide Web, pero por diferentes causas no fue posible.​ Por ese motivo introdujo el concepto de semántica con la intención de recuperar dicha omisión.\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantika Reto\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/HTML\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_resource\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Uniform_Resource_Identifier\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Extensibility\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://it.dbpedia.org/resource/Web_semantico\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/RDFa\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Knowledge_engineering\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:About\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/File:RDF_example_extended.svg\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/World_Wide_Web_Consortium\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/infocom yes\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Spamming\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Cascading_Style_Sheets\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Hyperdata\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ko.dbpedia.org/resource/시맨틱_웹\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/JSON-LD\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Scholia\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Семанти́ческая паути́на (от англ. semantic web) — общедоступная глобальная семантическая сеть, формируемая на базе Всемирной паутины путём стандартизации представления информации в виде, пригодном для машинной обработки. В обычной Всемирной паутине, основанной на HTML-страницах, информация заложена в тексте страниц и предназначена для чтения и понимания человеком. Семантическая паутина состоит из машинно-читаемых элементов — узлов семантической сети, с опорой на онтологии. Благодаря этому программы-клиенты получают возможность непосредственно получать из интернета утверждения вида «предмет — вид взаимосвязи — другой предмет» и вычислять по ним логические заключения. Семантическая паутина работает параллельно с обычной Всемирной паутиной и на её основе, используя протокол HTTP и идентификаторы ресурсов URI. Название «Семантическая паутина» было впервые введено Тимом Бернерсом-Ли (изобретателем Всемирной паутины) в сентябре 1998 года, и называется им «следующим шагом в развитии Всемирной паутины». Позже в своём блоге он предложил в качестве синонима термин «гигантский глобальный граф» (англ. giant global graph, GGG, по аналогии с WWW). Концепция семантической паутины была принята и продвигается консорциумом Всемирной паутины.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/ERDF_(data_format)\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Activity100407535\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://pl.dbpedia.org/resource/Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Knowledge_engineering\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Microdata_(HTML)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ontology_learning\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://tr.dbpedia.org/resource/Anlamsal_ağ\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_HTML\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://simple.dbpedia.org/resource/Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Thing\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Internet_ages\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract إن ورود المعلومات على الإنترنت يزداد بشكل كبير، فقد أصبح الإنترنت مكانًا للتعبير عن الأفكار، سرد القصص، إنشاء المدونات ومشاركة الفيديوهات والصور والملفات الصوتية وما إلى ذلك. وهو ما جعل كمّ المعلومات المتوفرة للفرد الواحد أكبر بكثير مما يُمكن له أن يستفيد منه. تعريض العقل البشري لهذا الكم الهائل من المعلومات من شأنه أن يتسبب فيما يُمكن أن نصفه بوصف «الضياع في فضاء المعلومات»، وذلك راجع إلى بقاء المعلومات المفيدة بعيدة المنال بسبب تراكم الكثير من المعلومات غير المفيدة وغير المرتبطة بالموضوع المراد البحث عنه من قبل المستخدم. لحسن الحظ، مثلما يزداد ورود المعلومات، تزداد مقدرات معالجة المعلومات اوتوماتيكياً، لذا يوجد إمكانيات كبيرة للاستفادة من مقدرات الأتمتة هذه بهدف استخراج المعلومات والخدمات من فيضان الويب والمرتبطة بالمستخدم، وتوصيلها إليه عن طريق واجهة مستخدم معيارية (Standardized User Interface). إن أهمية الحصول على المعلومات بهذه الطريقة التكيفية يزداد بازدياد كتلة المعلومات المتوفرة على الإنترنت. تعتبر شبكة الويب أغنى المصادر المعلوماتية بما تحويه من مستندات ومعلومات ومصادر منوعة يمكن الوصول إليها عن طريق محركات البحث التقليدية. غير أن تنظيم هذه المعلومات والمستندات بصورة تسهل عملية البحث فيها والوصول إليها، يعتبر أمراً غاية في الصعوبة. يضاف إلى ذلك، أنه في ظل التزايد المستمر في حجم المعلومات المنشورة في شبكة الويب أصبح من الصعوبة بمكان قيام محركات البحث بإيجاد المعلومات المناسبة. ومن هذه المشكلة ظهرت فكرة «الويب ذات الدلالات والمعاني اللفظية»، أو ما يطلق عليه بالإنجليزية مصطلح (Semantic Web)، والتي هي امتداد للويب الحالية ولكن تختلف عنها بأنها تتفهم مدلولات الألفاظ والمعاني البشرية.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Sensor_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Cryptography\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/s no\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_Ontology_Language\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Calais_(Reuters_product)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Machine-readable_data\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Nonsense106607339\n", - "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Internet_ages\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/ontology/MusicGenre\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Entity–attribute–value_model\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Tag\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Timo_Honkela\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantiska webben\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Ο Σημασιολογικός Ιστός (Web 3.0 ή Semantic Web στα αγγλικά) είναι μια επέκταση του σημερινού Ιστού, που θα φέρει δομή στο ουσιαστικό περιεχόμενο των ιστοσελίδων. Η λογική πίσω από αυτό είναι ότι η δημοσιευμένη πληροφορία θα περιέχει μεταδεδομένα, τα οποία θα είναι κοινά για όλους, θα μπορούν να «κατανοούνται» και από μηχανές, οι οποίες θα βοηθήσουν στην καλύτερη συλλογή και επεξεργασία τους. Στον επιχειρηματικό τομέα, θα υπάρχει καλύτερη οργάνωση των εταιριών, καλύτερες εμπειρίες για τους χρήστες στις διαδικτυακές αγορές και καλύτερος συντονισμός μεταξύ διαφορετικών εταιριών.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semantiko\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Scalability\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Elizabeth_F._Loftus\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Peter_Gärdenfors\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Семантическая паутина\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://simple.dbpedia.org/resource/Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_triple\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/%22\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Le Web sémantique, ou toile sémantique, est une extension du Web standardisée par le World Wide Web Consortium (W3C). Ces standards encouragent l'utilisation de formats de données et de protocoles d'échange normés sur le Web, en s'appuyant sur le modèle Resource Description Framework (RDF). Le Web sémantique est par certains qualifié de Web 3.0. Selon le W3C, « le Web sémantique fournit un modèle qui permet aux données d'être partagées et réutilisées entre plusieurs applications, entreprises et groupes d'utilisateurs ». L'expression a été inventée par Tim Berners-Lee (inventeur du Web et directeur du W3C), qui supervise le développement des technologies communes du Web sémantique. Il le définit comme « une toile de données qui peuvent être traitées directement et indirectement par des machines pour aider leurs utilisateurs à créer de nouvelles connaissances ». Pour y parvenir, le Web sémantique met en œuvre le Web des données qui consiste à lier et structurer l'information sur Internet pour accéder simplement à la connaissance qu'elle contient déjà. Alors que ses détracteurs ont mis en doute sa faisabilité, ses promoteurs font valoir que les applications réalisées par les chercheurs dans l'industrie, la biologie et les sciences humaines et sociales ont déjà prouvé la validité de ce nouveau concept. L'article original de Tim Berners-Lee en 2001 dans le Scientific American a décrit une évolution attendue du Web existant vers un Web sémantique, mais cela n'a pas encore eu lieu. En 2006, Tim Berners-Lee et ses collègues ont déclaré : « Cette idée simple… reste largement inexploitée. »\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://bn.dbpedia.org/resource/সেম্যান্টিক_ওয়েব\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Altavista\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://az.dbpedia.org/resource/Semantik_veb\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/q no\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/WikicatBuzzwords\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_crawler\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Web_Rule_Language\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Cite_book\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_browser\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://is.dbpedia.org/resource/Merkingarvefur\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/XHTML\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_2.0\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Privacy\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_pages\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantic Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikt no\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Internet_of_things\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Linked_data\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Turtle_(syntax)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/HTTP\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://sk.dbpedia.org/resource/Sémantický_web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Semantic Web (sieci semantyczne) – projekt, który ma przyczynić się do utworzenia i rozpowszechnienia standardów opisywania treści w Internecie w sposób, który umożliwi maszynom i programom (np. tzw. agentom) przetwarzanie informacji w sposób odpowiedni do ich znaczenia. Wśród standardów sieci semantycznych znajdują się m.in. OWL, RDF, RDF Schema (inaczej RDFS). Znaczenia zasobów informacyjnych określa się za pomocą tzw. ontologii. Sieć semantyczna jest wizją Tima Bernersa-Lee (twórcy standardu WWW i pierwszej przeglądarki internetowej, a także przewodniczącego W3C). W swoich założeniach sieć semantyczna ma korzystać z istniejącego protokołu komunikacyjnego, na którym bazuje dzisiejszy Internet. Różnica miałaby polegać na tym, że przesyłane dane mogłyby być 'rozumiane' także przez maszyny. Owo 'rozumienie' polegałoby na tym, że dane przekazywane byłyby w postaci, w której można by powiązać ich znaczenia między sobą, a także w ramach odpowiedniego kontekstu. Informacje przekazywane w ramach sieci semantycznej wymagałyby nie tylko samych danych, ale także informacji o tychże (tzw. meta-danych). To właśnie meta-dane zawierałyby sformułowania dotyczące relacji między danymi oraz prawa logiki, które można do nich zastosować. Dzięki temu można by: \n", + "* powiązać różne dane znajdujące się w Internecie w ramach wspólnych jednostek znaczeniowych (np. strony dotyczące filmów, dziedzin nauki, kuchni francuskiej, etc.) \n", + "* rozróżnić dane, które dla maszyn są w tej chwili nierozróżnialne ze względu na identyczny zapis tekstowy (np. zamek - urządzenie do zamykania drzwi; urządzenie do łączenia w ustalonym położeniu elementów ubrania; okazała budowla mieszkalno-obronna) \n", + "* przeprowadzać na tychże danych wnioskowania, tzn. otrzymywać informacje na ich temat, które nie są zawarte (np. na podstawie danej \"Ewa jest żoną Adama\", możemy też dowiedzieć się, że Ewa jest kobietą, Adam mężczyzną, Adam jest mężem Ewy, żaden inny mężczyzna nie jest mężem Ewy, etc.)\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/PsychologicalFeature100023100\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://zh.dbpedia.org/resource/语义网\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Business_semantics_management\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_triple\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Sister_project_links\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Information_retrieval\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Web_services\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Tag_(metadata)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Italics\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Trust_service\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Principle_of_explosion\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Smart-M3\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_search_engine\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_social_network\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_MediaWiki\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.wikidata.org/entity/Q1731%22%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Service100577525\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/b Semantic Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract A Web semântica é uma extensão da World Wide Web que permite aos computadores e humanos trabalharem em cooperação. Ela interliga significados de palavras e, neste âmbito, tem como finalidade conseguir atribuir um significado (sentido) aos conteúdos publicados na Internet de modo que seja compreensível tanto pelo humano como pelo computador. A ideia da Web semântica surgiu em 2001, quando Tim Berners-Lee, James Hendler e Ora Lassila publicaram um artigo na revista Scientific American, intitulado: “Web Semântica: um novo formato de conteúdo para a Web que tem significado para computadores vai iniciar uma revolução de novas possibilidades” (The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities). A web semântica é a relação de interatividade do homem e o computador, onde um depende do outro para a conclusão de tarefas, assim ocorrendo a acepção de entendimento do comando estipulado do ser humano à interface operacional da máquina, dando um significado de ordem a ser executada. Sendo desta forma uma espécie de escrita de entendimento tanto para o computador e para usuário, por meio de organização das informações contidas na máquina, através do seu perfil de suas últimas pesquisas realizadas, originando um repositório de dados ligados, como se fosse um verbete de enciclopédia. Podemos fazer uma ligação da web semântica com a Ciência da Informação, pelo fato de possuir a ideia estrutural de arranjo sistêmico, agrupando um conjunto de coleta de análise, classificação, armazenamento, disseminação e recuperação de informação como se fosse uma enciclopédia. Esta ligação, entre a Ciência da Informação e a Web Semântica, pode ser encontrada no projeto da Rede Universal de Documentação de Paul Otlet, que assenta nos mesmos ideais. O objetivo principal da web semântica não é treinar as máquinas para que se comportem como pessoas, mas sim desenvolver tecnologias e linguagens que tornem a informação legível para as máquinas. A finalidade passa pelo desenvolvimento de um modelo tecnológico que permita a partilha global de conhecimento assistido por máquinas. A integração das linguagens ou tecnologias eXtensible Markup Language (XML), Resource Description Framework (RDF), arquiteturas de metadados, ontologias, agentes computacionais, entre outras, favorecerá o aparecimento de serviços Web que garantam a interoperabilidade e cooperação.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Social_Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Semantic Web (sieci semantyczne) – projekt, który ma przyczynić się do utworzenia i rozpowszechnienia standardów opisywania treści w Internecie w sposób, który umożliwi maszynom i programom (np. tzw. agentom) przetwarzanie informacji w sposób odpowiedni do ich znaczenia. Wśród standardów sieci semantycznych znajdują się m.in. OWL, RDF, RDF Schema (inaczej RDFS). Znaczenia zasobów informacyjnych określa się za pomocą tzw. ontologii.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/OpenAlex\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract 시맨틱 웹(Semantic Web)은 '의미론적인 웹'이라는 뜻으로,현재의 인터넷과 같은 분산환경에서 리소스(웹 문서, 각종 파일, 서비스 등)에 대한 정보와 자원 사이의 관계-의미 정보(Semanteme)를 기계(컴퓨터)가 처리할 수 있는 온톨로지 형태로 표현하고, 이를 자동화된 기계(컴퓨터)가 처리하도록 하는 프레임워크이자 기술이다. 웹의 창시자인 팀 버너스리가 1998년 제안했고 현재 W3C에 의해 표준화 작업이 진행 중이다.\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://cs.dbpedia.org/resource/Sémantický_web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://sv.dbpedia.org/resource/Semantiska_webben\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/AGRIS\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Defeasible_reasoning\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Resource_Description_Framework\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/W3C\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract セマンティック・ウェブ（英: semantic web）は W3C のティム・バーナーズ＝リーによって提唱された、ウェブページの意味を扱うことを可能とする標準やツール群の開発によってワールド・ワイド・ウェブの利便性を向上させるプロジェクト。セマンティック・ウェブの目的はウェブページの閲覧という行為に、データの交換の側面に加えて意味の疎通を付け加えることにある。 現在のワールド・ワイド・ウェブ上のコンテンツは主にHTMLで記述されている。HTMLでは文書構造を伝えることは可能だが、個々の単語の意味をはじめとする詳細な意味を伝えることはできない。これに対し、セマンティック・ウェブはXMLによって記述した文書にRDFやOWLを用いてタグを付け加える。この、データの意味を記述したタグが文書の含む意味を形式化し、コンピュータによる自動的な情報の収集や分析へのアプローチが可能となると期待されている。オントロジーを扱う階層まではW3Cにより標準化されているが、それ以上の階層の開発は難しいため、実現と標準化には長期間掛かると予想されている。また、既存のWebサイトに対するメタデータ付与の作業が必要であるため、Web全域への普及に関しても長期間掛かると予想されている。 セマンティックウェブはXML、XML Schema、RDF、、OWLなどの標準およびツール群から構成されている。「OWL ウェブ・オントロジー言語概要」はセマンティックウェブにおけるこれら標準およびツール群の機能・関連について述べている。 近年では、Google検索のリッチスニペットなどの応用例が存在する。\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Семантична павутина\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semántica\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://schema.org/Person\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://xmlns.com/foaf/0.1/Person%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://az.dbpedia.org/resource/Semantik_veb\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://data.wordlift.io/wl0216/entity/semantic_web\n", - "http://dbpedia.org/resource/Semantic_Web http://xmlns.com/foaf/0.1/depiction http://commons.wikimedia.org/wiki/Special:FilePath/Semantic_web_stack.svg\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Con il termine web semantico, termine coniato dal suo ideatore, Tim Berners-Lee, si intende la trasformazione del World Wide Web in un ambiente dove i documenti pubblicati (pagine HTML, file, immagini, e così via) sono associati ad informazioni e dati (metadati) che ne specificano il contesto semantico in un formato adatto all'interrogazione e all'interpretazione (es. tramite motori di ricerca) e, più in generale, all'elaborazione automatica.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web3\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/List_of_emerging_technologies\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/RDF_Schema\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Psychologist\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Nextbio\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://schema.org/Person%3E\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Scalable_vector_graphics\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Div_col\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment The Semantic Web, sometimes known as Web 3.0 (not to be confused with Web3), is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable. Berners-Lee originally expressed his vision of the Semantic Web in 1999 as follows:\n", + "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Knowledge_engineering\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semântica\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Aaron_Swartz\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://example.org/semantic-web/%22%3ESemantic\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Semantiska webben är ett begrepp myntat av World Wide Web Consortiums (W3C) chef Tim Berners-Lee, som också är skapare av världswebben (WWW). Begreppet beskriver metoder och teknik för att möjliggöra för maskiner att förstå innebörden eller \"semantiken\" i informationen på webben. Den ursprungliga visionen var att tillgången på maskinläsbara metadata skulle möjliggöra för automatiska agenter och annan programvara att ansluta till internet på ett mer intelligent sätt. Även om termen \"semantiska webben\" inte är formellt definierad, så används den för att beskriva den modell och de tekniker som föreslagits av W3C. Dessa tekniker inkluderar Resource Description Framework (RDF), olika format för datautbyte (till exempel RDF / XML, JSON-LD, Notation 3, Turtle, N-Tripplar och notationer som RDF Schema och Web Ontology Language (OWL), som alla syftar till att ge en formell beskrivning av koncept, termer och relationer inom ett givet kunskapsområde. Många av de tekniker som föreslagits av W3C finns redan och används i olika projekt. Den semantiska webben som en global vision har dock inte ännu realiserats, och dess kritiker har ifrågasatt om tillvägagångssättet är möjligt. Dessutom har andra tekniker med liknande mål, till exempel mikroformat, utvecklats, vilka inte alltid beskrivs som \"den semantiska webben\".\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Distributed_computing\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Sémantický web se má stát novým evolučním stupněm stávajícího webu. Jedná se o web, kde jsou informace strukturovány a uloženy podle standardizovaných pravidel, což usnadňuje jejich vyhledání a zpracování. Staví zejména na Resource Description Framework (RDF) a (OWL). Na specifikacích pro sémantický web pracuje World Wide Web Consortium (W3C).\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_base\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_annotation\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Web_(journal)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Information_retrieval\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Tim_O'Reilly\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Web semantikoa (semantic web) World Wide Web Consortiumen muinean garatutako jarduera multzo bat da, aplikazio informatikoek datu irakurgarriak argitaratzeko teknologia bermatzen duena. World Wide Webari metadatu ontologiko eta semantikoak gehitzeko ideian oinarritzen da. Helburua interneta hobetzea da sistema informatikoen elkargarritasuna handituz, eragile adimenduak erabiliz. Eragile adimenduak informazioa era automatikoan eta gizakien laguntzarik gabe bilatzen duten programa informatikoak dira.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Simple_Knowledge_Organization_System\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/YagoPermanentlyLocatedEntity\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/N-Triples\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Semantika Reto (Angle:Semantic web) estas nova koncepto pri evoluo de Interreto, kiun alprenis kaj disvastigas Konsorcio de la Tutmonda Teksaĵo. Iufoje oni ĝin nomas Semantika Teksaĵo, Semantika Plekto.Tiu koncepto permesas publikigi, konsulti, kaj speciale aŭtomatigi la traktadon de sciadoj strukturitaj.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Rule_Interchange_Format\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_management\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Tim_Berners-Lee\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_domain\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Identifier\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_translation\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/RDF/XML\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/EU_Open_Data_Portal\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://hu.dbpedia.org/resource/Szemantikus_web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ontology_(information_science)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Reflist\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Web Semantik (Web Bermakna) merujuk kepada teknik yang memungkinkan konten pada Web untuk dapat lebih dimengerti oleh komputer. Istilah Web Semantik itu sendiri dicetuskan oleh Tim Berners-Lee, penemu World Wide Web. Sekarang, prinsip Web Semantik disebut-sebut akan muncul pada Web 3.0, generasi ketiga dari World Wide Web. Bahkan, Web 3.0 itu sendiri sering disamakan dengan Web Semantik. Teknologi Web Semantik ini antara lain adalah RDF, OWL dan SPARQL.\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Credibility\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Ο Σημασιολογικός Ιστός (Web 3.0 ή Semantic Web στα αγγλικά) είναι μια επέκταση του σημερινού Ιστού, που θα φέρει δομή στο ουσιαστικό περιεχόμενο των ιστοσελίδων. Η λογική πίσω από αυτό είναι ότι η δημοσιευμένη πληροφορία θα περιέχει μεταδεδομένα, τα οποία θα είναι κοινά για όλους, θα μπορούν να «κατανοούνται» και από μηχανές, οι οποίες θα βοηθήσουν στην καλύτερη συλλογή και επεξεργασία τους. Ο Σημασιολογικός Ιστός βασίζεται σε τεχνολογίες που ήδη υπάρχουν (URI και XML) αλλά και σε νέες τεχνολογίες (RDF, RDFS, OWL, κα.), οι οποίες αναπτύσσονται με την βοήθεια της κοινότητας. Δεδομένου ότι ο νέος Ιστός σκοπεύει να είναι μια μεγάλη βάση όπου δεδομένα από διαφορετικά πεδία θα συνδέονται μεταξύ τους, αναμένεται να παίξει μεγάλο ρόλο στη ζωή μας. Μερικά από τα πεδία στα οποία αναμένεται να έχει την μεγαλύτερη επίδραση είναι στην υγεία, στην παιδεία και στις επιχειρήσεις. Υπάρχουν ήδη πολλές προσπάθειες από εταιρίες, ερευνητές και μη κερδοσκοπικές οργανώσεις για να παραγάγουν πρότυπα οντολογιών, κυρίως για τα παραπάνω πεδία, για να υπάρχουν κοινές γλώσσες και περισσότερα δεδομένα τα οποία να μπορούν να συνδυαστούν για καλύτερα αποτελέσματα. Στην υγεία, γίνεται προσπάθεια για τη δημιουργία ενοποιημένων γλωσσών ιατρικής ορολογίας και υπηρεσίες που θα βοηθάνε το ιατρικό προσωπικό και θα κατευθύνουν τους καταναλωτές σε αξιόπιστες πληροφορίες υγείας σχετικά με την κατάστασή τους. Στην εκπαίδευση, ο Σημασιολογικός Ιστός θα συμβάλει σημαντικά στην μάθηση κυρίως στον τρόπο αναζήτησης πληροφοριών, στην οργάνωση των αποτελεσμάτων και στη δημιουργία ενός προγράμματος μάθησης ειδικό για το καθένα. Στον επιχειρηματικό τομέα, θα υπάρχει καλύτερη οργάνωση των εταιριών, καλύτερες εμπειρίες για τους χρήστες στις διαδικτυακές αγορές και καλύτερος συντονισμός μεταξύ διαφορετικών εταιριών. Στην καθημερινότητά θα υπάρχουν επιδράσεις του Web 3.0 στα κοινωνικά δίκτυα και εικονικές κοινότητες. Θα υπάρχουν εφαρμογές, οι οποίες θα δίνουν περισσότερες, πιο έμπιστες, πληροφορίες και θα διευκολύνουν σημαντικά τις διαδικτυακές δραστηριότητες.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikt no\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/ns/prov#wasDerivedFrom http://en.wikipedia.org/wiki/Semantic_Web?oldid=1124065133&ns=0\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Free_University_of_Berlin\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Cryptography\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_domain\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/James_Hendler\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Sister_project_links\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_integration\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantic Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/GRDDL\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageRevisionID 1124065133\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Con il termine web semantico, termine coniato dal suo ideatore, Tim Berners-Lee, si intende la trasformazione del World Wide Web in un ambiente dove i documenti pubblicati (pagine HTML, file, immagini, e così via) sono associati ad informazioni e dati (metadati) che ne specificano il contesto semantico in un formato adatto all'interrogazione e all'interpretazione (es. tramite motori di ricerca) e, più in generale, all'elaborazione automatica. Con l'interpretazione del contenuto dei documenti che il Web semantico impone, saranno possibili ricerche molto più evolute delle attuali, basate sulla presenza nel documento di parole chiave, e altre operazioni specialistiche come la costruzione di reti di relazioni e connessioni tra documenti secondo logiche più elaborate del semplice collegamento ipertestuale.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Frank_van_Harmelen\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://lv.dbpedia.org/resource/Semantiskais_tīmeklis\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Linked_Open_Data\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/thumbnail http://commons.wikimedia.org/wiki/Special:FilePath/RDF_example.svg?width=300\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://sk.dbpedia.org/resource/Sémantický_web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://www.wikidata.org/entity/Q54837\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Scientific_American\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://www.w3.org/2002/07/owl%23equivalentClass%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Institut_national_de_recherche_en_informatique_et_en_automatique\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Web_Stack\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://fr.dbpedia.org/resource/Web_sémantique\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Intelligent_agent\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/ontology/Software\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Das Semantic Web erweitert das Web, um Daten zwischen Rechnern einfacher austauschbar und für sie einfacher verwertbar zu machen; so kann beispielsweise das Wort „Bremen“ in einem Webdokument um die Information ergänzt werden, ob hier der Begriff des Schiffs-, Familien- oder Stadtnamens gemeint ist. Diese zusätzlichen Informationen explizieren die sonst nur unstrukturiert vorkommenden Daten. Zur Realisierung dienen Standards zur Veröffentlichung und Nutzung maschinenlesbarer Daten (insbesondere RDF).Während Menschen solche Informationen aus dem gegebenen Kontext schließen können (aus dem Gesamttext, über die Art der Publikation oder der Rubrik in selbiger, Bilder etc.) und derartige Verknüpfungen unbewusst aufbauen, muss Maschinen dieser Kontext erst beigebracht werden; hierzu werden die I\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract The Semantic Web, sometimes known as Web 3.0 (not to be confused with Web3), is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable. To enable the encoding of semantics with the data, technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used. These technologies are used to formally represent metadata. For example, ontology can describe concepts, relationships between entities, and categories of things. These embedded semantics offer significant advantages such as reasoning over data and operating with heterogeneous data sources. These standards promote common data formats and exchange protocols on the Web, fundamentally the RDF. According to the W3C, \"The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.\" The Semantic Web is therefore regarded as an integrator across different content and information applications and systems. The term was coined by Tim Berners-Lee for a web of data (or data web) that can be processed by machines—that is, one in which much of the meaning is machine-readable. While its critics have questioned its feasibility, proponents argue that applications in library and information science, industry, biology and human sciences research have already proven the validity of the original concept. Berners-Lee originally expressed his vision of the Semantic Web in 1999 as follows: I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A \"Semantic Web\", which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The \"intelligent agents\" people have touted for ages will finally materialize. The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web. In 2006, Berners-Lee and colleagues stated that: \"This simple idea…remains largely unrealized\".In 2013, more than four million Web domains (out of roughly 250 million total) contained Semantic Web markup.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Human_science\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Ontology_learning\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Library_science\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://archive.org/details/isbn_9780470396797\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_resource\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract إن ورود المعلومات على الإنترنت يزداد بشكل كبير، فقد أصبح الإنترنت مكانًا للتعبير عن الأفكار، سرد القصص، إنشاء المدونات ومشاركة الفيديوهات والصور والملفات الصوتية وما إلى ذلك. وهو ما جعل كمّ المعلومات المتوفرة للفرد الواحد أكبر بكثير مما يُمكن له أن يستفيد منه. تعريض العقل البشري لهذا الكم الهائل من المعلومات من شأنه أن يتسبب فيما يُمكن أن نصفه بوصف «الضياع في فضاء المعلومات»، وذلك راجع إلى بقاء المعلومات المفيدة بعيدة المنال بسبب تراكم الكثير من المعلومات غير المفيدة وغير المرتبطة بالموضوع المراد البحث عنه من قبل المستخدم. لحسن الحظ، مثلما يزداد ورود المعلومات، تزداد مقدرات معالجة المعلومات اوتوماتيكياً، لذا يوجد إمكانيات كبيرة للاستفادة من مقدرات الأتمتة هذه بهدف استخراج المعلومات والخدمات من فيضان الويب والمرتبطة بالمستخدم، وتوصيلها إليه عن طريق واجهة مستخدم معيارية (Standardized User Interface). إن أهمية الحصول على المعلومات بهذه الطريقة التكيفية يزداد بازدياد كتلة المعلومات المتوفرة على الإنترنت. تعتبر شبكة الويب أغنى المصادر المعلوماتية بما تحويه من مستندات ومعلومات ومصادر منوعة يمكن الوصول إليها عن طريق محركات البحث التقليدية. غير أن تنظيم هذه المعلومات والمستندات بصورة تسهل عملية البحث فيها والوصول إليها، يعتبر أمراً غاية في الصعوبة. يضاف إلى ذلك، أنه في ظل التزايد المستمر في حجم المعلومات المنشورة في شبكة الويب أصبح من الصعوبة بمكان قيام محركات البحث بإيجاد المعلومات المناسبة. ومن هذه المشكلة ظهرت فكرة «الويب ذات الدلالات والمعاني اللفظية»، أو ما يطلق عليه بالإنجليزية مصطلح (Semantic Web)، والتي هي امتداد للويب الحالية ولكن تختلف عنها بأنها تتفهم مدلولات الألفاظ والمعاني البشرية.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_pages\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/File:RDF_example_extended.svg\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment 语义网（英语：Semantic Web）是由万维网联盟的蒂姆·伯纳斯-李（Tim Berners-Lee）在1998年提出的一个概念，它的核心是：通过给万维网上的文档（如: HTML文档）添加能够被计算机所理解的语义（元数据），从而使整个互联网成为一个通用的信息交换媒介。语义万维网通过使用标准、置标语言和相关的处理工具来扩展万维网的能力。不过语意网概念实际上是基于很多已有技术的，也依赖于后来和text-and-markup与知识表现的综合。 \"语义\"网是由比现今成熟的网际搜索工具更加行之有效的、更加广泛意义的并且自动聚集和搜集信息的文档组成的。其最基本的元素就是。\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/File_system\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://eu.dbpedia.org/resource/Web_semantiko\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_MediaWiki\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Paraconsistent_logic\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Attribute100024264\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-662-43795-7\n", + "http://dbpedia.org/resource/Semantic_Web http://xmlns.com/foaf/0.1/isPrimaryTopicOf http://en.wikipedia.org/wiki/Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/WikicatWebServices\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Buzzword106608277\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Business_semantics_management\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/DBpedia\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.wikidata.org/entity/Q1731%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Act100030358\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Семантична павутина\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Automated_reasoning_system\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://fi.dbpedia.org/resource/Semanttinen_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageRevisionID 1124065133\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Citation_needed\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/ActivityPub\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label セマンティック・ウェブ\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantics\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Peter_Gärdenfors\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Schema.org\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Geospatial_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageInterLanguageLink http://commons.dbpedia.org/resource/File:Aaron_Swartz_s_A_Programmable_Web_An_Unfinished_Work.pdf\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Семантична павутина (англ. Semantic web) — нова концепція розвитку Всесвітньої павутини і мережі Інтернет, яка створена і впроваджується Консорціумом Всесвітньої павутини (англ. World Wide Web Consortium, W3C). Інші назви — семантичний веб, семантична мережа. Хоча поняття семантична мережа, яке виникло раніше, породило поняття семантична павутина, їх слід відокремлювати. Концепція полягає у впровадженні спільних, стандартних форматів даних у Мережі. Для заохочення впровадження семантичного форматування сторінок, пропонується змінювати структуру вже існуючих, не структурованих чи частково-структурованих сторінок у «мережу даних». Створення семантичної Мережі полягає у застосуванні середовища опису ресурсів (RDF). Термін вперше запровадив Тім Бернерс-Лі в травні 2001 року в журналі «Scientific American»\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:About\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:R\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageID 29123\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://hu.dbpedia.org/resource/Szemantikus_web\n", - "http://dbpedia.org/resource/Semantic_Web http://xmlns.com/foaf/0.1/depiction http://commons.wikimedia.org/wiki/Special:FilePath/RDF_example_extended.svg\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/XML\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Library_science\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Div_col_end\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Unicode\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Resource_(computer_science)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Resource_Description_Framework\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:'%22\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Folksonomy\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Category:Internet_ages\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Sémantický web se má stát novým evolučním stupněm stávajícího webu. Jedná se o web, kde jsou informace strukturovány a uloženy podle standardizovaných pravidel, což usnadňuje jejich vyhledání a zpracování. Staví zejména na Resource Description Framework (RDF) a (OWL). Na specifikacích pro sémantický web pracuje World Wide Web Consortium (W3C). Myšlenku sémantického, neboli významového webu poprvé vyslovil Tim Berners-Lee v roce 2001, kdy upozornil na skutečnost, že současný web je pouze změť webových stránek, která neustále roste a ve které je stále složitější nalézt relevantní informace. Sémantický web je podle něj rozšířením současného webu, v němž informace mají přidělen dobře definovaný význam lépe umožňující počítačům a lidem spolupracovat. Sémantický web představuje reprezentaci dat na WWW. Je založen na technologii Resource Description Framework (RDF), která integruje širokou škálu aplikací využívajících syntaktický zápis v XML a identifikátory URI pro pojmenovávání. Znamená to tedy, že data prezentovaná na Internetu by měla mít přesně definovaný význam a dovolovat do značné míry automatizované (strojové) zpracování, které by se mělo realizovat pomocí softwarových agentů. Základním krokem k vytvoření sémantického webu je konceptualizace dat dostupných na Internetu, jejíž klíčovým nástrojem jsou ontologie, aneb formalizované reprezentace znalostí určené k jejich sdílení a znovupoužití. Sémantický web je dále založen na standardizovaném popisu webových zdrojů (vše, dosažitelné pomocí WWW, tedy textové dokumenty, obrázky, videosekvence, zvukové soubory apod.). Každý zdroj by byl vybaven stejnými charakteristikami údaji (autor, typ zdroje, klíčová slova atd.), což by umožnilo uživatelům Internetu pracovat se sítí WWW jako s relační databází a dotazovat se na její obsah prostřednictvím jazyků podobných SQL. Důraz by se kladl na vysokou přesnost a relevanci odpovědi na vyhledávací dotaz. Technologickým základem sémantického webu by se měl stát standard RDF (Resource Description Framework) - obecný rámec pro popis, výměnu a znovupoužití metadat. Poskytuje jednoduchý model pro popis zdrojů, který není závislý na konkrétní implementaci. Datový model RDF umožní specifikovat trojice (zdroj, vlastnost, hodnota vlastnosti). Tímto se jedná mimo jiné o přiřazení sémantiky webovým zdrojům, což je pro sémantický web klíčové. Sémantický web propaguje Tim Berners-Lee již řadu let, přesto se dosud nedočkal výrazného rozmachu, pravděpodobně protože je oproti stávajícímu webu příliš komplikovaný. Proto vznikají jednodušší způsoby, jak do stávajícího webu snadno přidat sémantickou informaci: Mikroformáty a RDFa.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://id.dbpedia.org/resource/Web_semantik\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://he.dbpedia.org/resource/רשת_סמנטית\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://da.dbpedia.org/resource/Semantisk_web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Meta-data\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Intelligent_agent\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Notation3\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://eu.dbpedia.org/resource/Web_semantiko\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_annotation\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://bn.dbpedia.org/resource/সেম্যান্টিক_ওয়েব\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Cory_Doctorow\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/topics yes\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Age104924103\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Web Semantik (Web Bermakna) merujuk kepada teknik yang memungkinkan konten pada Web untuk dapat lebih dimengerti oleh komputer. Istilah Web Semantik itu sendiri dicetuskan oleh Tim Berners-Lee, penemu World Wide Web. Sekarang, prinsip Web Semantik disebut-sebut akan muncul pada Web 3.0, generasi ketiga dari World Wide Web. Bahkan, Web 3.0 itu sendiri sering disamakan dengan Web Semantik. Teknologi Web Semantik ini antara lain adalah RDF, OWL dan SPARQL.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/ERDF_(data_format)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/thumbnail http://commons.wikimedia.org/wiki/Special:FilePath/RDF_example.svg?width=300\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Con il termine web semantico, termine coniato dal suo ideatore, Tim Berners-Lee, si intende la trasformazione del World Wide Web in un ambiente dove i documenti pubblicati (pagine HTML, file, immagini, e così via) sono associati ad informazioni e dati (metadati) che ne specificano il contesto semantico in un formato adatto all'interrogazione e all'interpretazione (es. tramite motori di ricerca) e, più in generale, all'elaborazione automatica. Con l'interpretazione del contenuto dei documenti che il Web semantico impone, saranno possibili ricerche molto più evolute delle attuali, basate sulla presenza nel documento di parole chiave, e altre operazioni specialistiche come la costruzione di reti di relazioni e connessioni tra documenti secondo logiche più elaborate del semplice collegamento ipertestuale.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Calais_(Reuters_product)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/World_Wide_Web_Consortium\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/RDF_Schema\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Information_science\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://no.dbpedia.org/resource/Semantisk_web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/XML\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_heterogeneity\n", "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantisch web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/File:Semantic_web_stack.svg\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Description_logic\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/James_Hendler\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://d-nb.info/gnd/4688372-1\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://cacm.acm.org/magazines/2021/2/250085-a-review-of-the-semantic-web-field/fulltext%7Cdoi-access=free\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Event100029378\n", - "http://dbpedia.org/resource/Semantic_Web http://purl.org/linguistics/gold/hypernym http://dbpedia.org/resource/Extension\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Property104916342\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Work100575741\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Metacrap\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Wikidata\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract セマンティック・ウェブ（英: semantic web）は W3C のティム・バーナーズ＝リーによって提唱された、ウェブページの意味を扱うことを可能とする標準やツール群の開発によってワールド・ワイド・ウェブの利便性を向上させるプロジェクト。セマンティック・ウェブの目的はウェブページの閲覧という行為に、データの交換の側面に加えて意味の疎通を付け加えることにある。 現在のワールド・ワイド・ウェブ上のコンテンツは主にHTMLで記述されている。HTMLでは文書構造を伝えることは可能だが、個々の単語の意味をはじめとする詳細な意味を伝えることはできない。これに対し、セマンティック・ウェブはXMLによって記述した文書にRDFやOWLを用いてタグを付け加える。この、データの意味を記述したタグが文書の含む意味を形式化し、コンピュータによる自動的な情報の収集や分析へのアプローチが可能となると期待されている。オントロジーを扱う階層まではW3Cにより標準化されているが、それ以上の階層の開発は難しいため、実現と標準化には長期間掛かると予想されている。また、既存のWebサイトに対するメタデータ付与の作業が必要であるため、Web全域への普及に関しても長期間掛かると予想されている。 セマンティックウェブはXML、XML Schema、RDF、、OWLなどの標準およびツール群から構成されている。「OWL ウェブ・オントロジー言語概要」はセマンティックウェブにおけるこれら標準およびツール群の機能・関連について述べている。 近年では、Google検索のリッチスニペットなどの応用例が存在する。\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://yago-knowledge.org/resource/Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantic Web\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Amazon_Mechanical_Turk\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Artificial_intelligence\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Mastodon_(software)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Deductive_reasoning\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_Navigator\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.wikidata.org/entity/Q1731%22%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Sensor_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Semantic Web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Notation3\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Hyperdata\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_web_service\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/name%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://cs.dbpedia.org/resource/Sémantický_web\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ja.dbpedia.org/resource/セマンティック・ウェブ\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Microformat\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Linked_Data\n", "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Emerging_technologies\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Use_American_English\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs https://global.dbpedia.org/id/4jzLX\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_science\n", - "http://dbpedia.org/resource/Semantic_Web http://xmlns.com/foaf/0.1/isPrimaryTopicOf http://en.wikipedia.org/wiki/Semantic_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://lt.dbpedia.org/resource/Semantinis_tinklas\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://www.w3.org/standards/semanticweb/\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/ontology/Software\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Branches_of_science\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/E-learning\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment A Web semântica é uma extensão da World Wide Web que permite aos computadores e humanos trabalharem em cooperação. Ela interliga significados de palavras e, neste âmbito, tem como finalidade conseguir atribuir um significado (sentido) aos conteúdos publicados na Internet de modo que seja compreensível tanto pelo humano como pelo computador. A ideia da Web semântica surgiu em 2001, quando Tim Berners-Lee, James Hendler e Ora Lassila publicaram um artigo na revista Scientific American, intitulado: “Web Semântica: um novo formato de conteúdo para a Web que tem significado para computadores vai iniciar uma revolução de novas possibilidades” (The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities).\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Distributed_artificial_intelligence\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/MIT_Press\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/Person%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment La web semántica (del inglés semantic web) es un conjunto de actividades desarrolladas en el seno de World Wide Web Consortium con tendencia a la creación de tecnologías para publicar datos legibles por aplicaciones informáticas (máquinas en la terminología de la Web semántica).​ Se basa en la idea de añadir metadatos semánticos y ontológicos a la World Wide Web. Esas informaciones adicionales —que describen el contenido, el significado y la relación de los datos— se deben proporcionar de manera formal, para que así sea posible evaluarlas automáticamente por máquinas de procesamiento. El objetivo es mejorar Internet ampliando la interoperabilidad entre los sistemas informáticos usando \"agentes inteligentes\". Agentes inteligentes son programas en las computadoras que buscan información sin \n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/File:Semantic_web_stack.svg\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/SNOMED_CT\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/World_Wide_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Schema.org\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/EU_Open_Data_Portal\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_heterogeneity\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label セマンティック・ウェブ\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/%22\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://fa.dbpedia.org/resource/وب_معنایی\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semantico\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/q no\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_matching\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Scalability\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://rdf.freebase.com/ns/m.076k0\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semàntic\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Div_col\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-662-43795-7\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ca.dbpedia.org/resource/Web_semàntic\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Wikidata\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/ns/prov#wasDerivedFrom http://en.wikipedia.org/wiki/Semantic_Web?oldid=1124065133&ns=0\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/RDF/XML\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Class_(computer_programming)\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Semantic Web (sieci semantyczne) – projekt, który ma przyczynić się do utworzenia i rozpowszechnienia standardów opisywania treści w Internecie w sposób, który umożliwi maszynom i programom (np. tzw. agentom) przetwarzanie informacji w sposób odpowiedni do ich znaczenia. Wśród standardów sieci semantycznych znajdują się m.in. OWL, RDF, RDF Schema (inaczej RDFS). Znaczenia zasobów informacyjnych określa się za pomocą tzw. ontologii. Sieć semantyczna jest wizją Tima Bernersa-Lee (twórcy standardu WWW i pierwszej przeglądarki internetowej, a także przewodniczącego W3C). W swoich założeniach sieć semantyczna ma korzystać z istniejącego protokołu komunikacyjnego, na którym bazuje dzisiejszy Internet. Różnica miałaby polegać na tym, że przesyłane dane mogłyby być 'rozumiane' także przez maszyny. Owo 'rozumienie' polegałoby na tym, że dane przekazywane byłyby w postaci, w której można by powiązać ich znaczenia między sobą, a także w ramach odpowiedniego kontekstu. Informacje przekazywane w ramach sieci semantycznej wymagałyby nie tylko samych danych, ale także informacji o tychże (tzw. meta-danych). To właśnie meta-dane zawierałyby sformułowania dotyczące relacji między danymi oraz prawa logiki, które można do nich zastosować. Dzięki temu można by: \n", - "* powiązać różne dane znajdujące się w Internecie w ramach wspólnych jednostek znaczeniowych (np. strony dotyczące filmów, dziedzin nauki, kuchni francuskiej, etc.) \n", - "* rozróżnić dane, które dla maszyn są w tej chwili nierozróżnialne ze względu na identyczny zapis tekstowy (np. zamek - urządzenie do zamykania drzwi; urządzenie do łączenia w ustalonym położeniu elementów ubrania; okazała budowla mieszkalno-obronna) \n", - "* przeprowadzać na tychże danych wnioskowania, tzn. otrzymywać informacje na ich temat, które nie są zawarte (np. na podstawie danej \"Ewa jest żoną Adama\", możemy też dowiedzieć się, że Ewa jest kobietą, Adam mężczyzną, Adam jest mężem Ewy, żaden inny mężczyzna nie jest mężem Ewy, etc.)\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web sémantique\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/commons Category:Semantic Web\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_Web_Stack\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.wikidata.org/entity/Q1731%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://pl.dbpedia.org/resource/Semantic_Web\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Scientific_American\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Hypertext\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Le Web sémantique, ou toile sémantique, est une extension du Web standardisée par le World Wide Web Consortium (W3C). Ces standards encouragent l'utilisation de formats de données et de protocoles d'échange normés sur le Web, en s'appuyant sur le modèle Resource Description Framework (RDF). Le Web sémantique est par certains qualifié de Web 3.0. Selon le W3C, « le Web sémantique fournit un modèle qui permet aux données d'être partagées et réutilisées entre plusieurs applications, entreprises et groupes d'utilisateurs ». L'expression a été inventée par Tim Berners-Lee (inventeur du Web et directeur du W3C), qui supervise le développement des technologies communes du Web sémantique. Il le définit comme « une toile de données qui peuvent être traitées directement et indirectement par des machines pour aider leurs utilisateurs à créer de nouvelles connaissances ». Pour y parvenir, le Web sémantique met en œuvre le Web des données qui consiste à lier et structurer l'information sur Internet pour accéder simplement à la connaissance qu'elle contient déjà. Alors que ses détracteurs ont mis en doute sa faisabilité, ses promoteurs font valoir que les applications réalisées par les chercheurs dans l'industrie, la biologie et les sciences humaines et sociales ont déjà prouvé la validité de ce nouveau concept. L'article original de Tim Berners-Lee en 2001 dans le Scientific American a décrit une évolution attendue du Web existant vers un Web sémantique, mais cela n'a pas encore eu lieu. En 2006, Tim Berners-Lee et ses collègues ont déclaré : « Cette idée simple… reste largement inexploitée. »\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Semantika Reto (Angle:Semantic web) estas nova koncepto pri evoluo de Interreto, kiun alprenis kaj disvastigas Konsorcio de la Tutmonda Teksaĵo. Iufoje oni ĝin nomas Semantika Teksaĵo, Semantika Plekto.Tiu koncepto permesas publikigi, konsulti, kaj speciale aŭtomatigi la traktadon de sciadoj strukturitaj.\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label Web semantik\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/v no\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Linguistics\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Intelligent_text_analysis\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Short_description\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://fi.dbpedia.org/resource/Semanttinen_Web\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Het semantisch web verschaft een framework waarmee gegevens kunnen worden gedeeld en hergebruikt. Het is een samenwerking onder leiding van het internationale orgaan voor internetstandaarden, het World Wide Web Consortium W3C. Het semantisch web is een uitbreiding van het internet, om de uitwisseling van gegevens tussen de deelnemers, tussen alle nodes, efficiënter te laten verlopen. Open data zijn de vrij beschikbare informatie, die door het semantisch web wordt gebruikt.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://uk.dbpedia.org/resource/Семантична_павутина\n", + "http://dbpedia.org/resource/Semantic_Web http://purl.org/dc/terms/subject http://dbpedia.org/resource/Category:Web_services\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract La web semántica (del inglés semantic web) es un conjunto de actividades desarrolladas en el seno de World Wide Web Consortium con tendencia a la creación de tecnologías para publicar datos legibles por aplicaciones informáticas (máquinas en la terminología de la Web semántica).​ Se basa en la idea de añadir metadatos semánticos y ontológicos a la World Wide Web. Esas informaciones adicionales —que describen el contenido, el significado y la relación de los datos— se deben proporcionar de manera formal, para que así sea posible evaluarlas automáticamente por máquinas de procesamiento. El objetivo es mejorar Internet ampliando la interoperabilidad entre los sistemas informáticos usando \"agentes inteligentes\". Agentes inteligentes son programas en las computadoras que buscan información sin operadores humanos. El precursor de la idea, Tim Berners-Lee, intentó desde el principio incluir información semántica en su creación, la World Wide Web, pero por diferentes causas no fue posible.​ Por ese motivo introdujo el concepto de semántica con la intención de recuperar dicha omisión.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract 语义网（英语：Semantic Web）是由万维网联盟的蒂姆·伯纳斯-李（Tim Berners-Lee）在1998年提出的一个概念，它的核心是：通过给万维网上的文档（如: HTML文档）添加能够被计算机所理解的语义（元数据），从而使整个互联网成为一个通用的信息交换媒介。语义万维网通过使用标准、置标语言和相关的处理工具来扩展万维网的能力。不过语意网概念实际上是基于很多已有技术的，也依赖于后来和text-and-markup与知识表现的综合。 \"语义\"网是由比现今成熟的网际搜索工具更加行之有效的、更加广泛意义的并且自动聚集和搜集信息的文档组成的。其最基本的元素就是。\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/HTML_element\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment El Web semàntic és un projecte que té com a objectiu crear un medi universal per a l'intercanvi d'informació significativa (semàntica), d'una forma comprensible per a les màquines, del contingut dels documents de la Web. Amb això es pretén ampliar la interoperabilitat dels sistemes informàtics i reduir la mediació dels operadors humans en els processos intel·ligents de flux d'informació. El pare de la idea, Tim Berners-Lee, creador del Web i fundador del W3C, proposa que la Web semàntica serveixi per a ampliar la capacitat de la World Wide Web mitjançant els , els llenguatges d'etiquetatge i altres eines de processament relacionades.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://www.w3.org/1999/02/22-rdf-syntax-ns%23type%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://lv.dbpedia.org/resource/Semantiskais_tīmeklis\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Le Web sémantique, ou toile sémantique, est une extension du Web standardisée par le World Wide Web Consortium (W3C). Ces standards encouragent l'utilisation de formats de données et de protocoles d'échange normés sur le Web, en s'appuyant sur le modèle Resource Description Framework (RDF). Le Web sémantique est par certains qualifié de Web 3.0.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/AGRIS\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Cite_book\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Cascading_Style_Sheets\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ru.dbpedia.org/resource/Семантическая_паутина\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantically-Interlinked_Online_Communities\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment إن ورود المعلومات على الإنترنت يزداد بشكل كبير، فقد أصبح الإنترنت مكانًا للتعبير عن الأفكار، سرد القصص، إنشاء المدونات ومشاركة الفيديوهات والصور والملفات الصوتية وما إلى ذلك. وهو ما جعل كمّ المعلومات المتوفرة للفرد الواحد أكبر بكثير مما يُمكن له أن يستفيد منه. تعريض العقل البشري لهذا الكم الهائل من المعلومات من شأنه أن يتسبب فيما يُمكن أن نصفه بوصف «الضياع في فضاء المعلومات»، وذلك راجع إلى بقاء المعلومات المفيدة بعيدة المنال بسبب تراكم الكثير من المعلومات غير المفيدة وغير المرتبطة بالموضوع المراد البحث عنه من قبل المستخدم. لحسن الحظ، مثلما يزداد ورود المعلومات، تزداد مقدرات معالجة المعلومات اوتوماتيكياً، لذا يوجد إمكانيات كبيرة للاستفادة من مقدرات الأتمتة هذه بهدف استخراج المعلومات والخدمات من فيضان الويب والمرتبطة بالمستخدم، وتوصيلها إليه عن طريق واجهة مستخدم معيارية (Standardized User In\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Anchor\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Tag\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/wikiPageUsesTemplate http://dbpedia.org/resource/Template:Emerging_technologies\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Semantika Reto (Angle:Semantic web) estas nova koncepto pri evoluo de Interreto, kiun alprenis kaj disvastigas Konsorcio de la Tutmonda Teksaĵo. Iufoje oni ĝin nomas Semantika Teksaĵo, Semantika Plekto.Tiu koncepto permesas publikigi, konsulti, kaj speciale aŭtomatigi la traktadon de sciadoj strukturitaj.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Web semantikoa (semantic web) World Wide Web Consortiumen muinean garatutako jarduera multzo bat da, aplikazio informatikoek datu irakurgarriak argitaratzeko teknologia bermatzen duena. World Wide Webari metadatu ontologiko eta semantikoak gehitzeko ideian oinarritzen da. Helburua interneta hobetzea da sistema informatikoen elkargarritasuna handituz, eragile adimenduak erabiliz. Eragile adimenduak informazioa era automatikoan eta gizakien laguntzarik gabe bilatzen duten programa informatikoak dira. Ideia honen aitzindaria, Tim Berners-Lee, hasieratik saiatu zen informazio semantikoa sartzen haren sorkuntzan (World Wide Web), baina arazo desberdinengatik ez zuen lortu. Arrazoi horrengatik landu zuen semantika.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/FOAF_(software)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Rule_Interchange_Format\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://dbpedia.org/class/yago/Event100029378\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Psychologist\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Inference\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label 시맨틱 웹\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Fuzzy_logic\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Semantic_computing\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/s no\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://xmlns.com/foaf/0.1/Person%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Class_(computer_programming)\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Smart-M3\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://sv.dbpedia.org/resource/Semantiska_webben\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Usability\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink http://www.w3.org/standards/semanticweb/\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://tr.dbpedia.org/resource/Anlamsal_ağ\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_engineering\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/property/d Q54837\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Web_engineering\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageExternalLink https://schema.org/itemtype%3E\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Knowledge_management\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/World_Wide_Web\n", "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Database\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://ja.dbpedia.org/resource/セマンティック・ウェブ\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Distributed_computing\n", - "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Semantiska webben är ett begrepp myntat av World Wide Web Consortiums (W3C) chef Tim Berners-Lee, som också är skapare av världswebben (WWW). Begreppet beskriver metoder och teknik för att möjliggöra för maskiner att förstå innebörden eller \"semantiken\" i informationen på webben. Den ursprungliga visionen var att tillgången på maskinläsbara metadata skulle möjliggöra för automatiska agenter och annan programvara att ansluta till internet på ett mer intelligent sätt.\n", - "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/SPARQL\n" + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#label 语义网\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://rdf.freebase.com/ns/m.076k0\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://www.wikidata.org/entity/Q54837\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2002/07/owl#sameAs http://vi.dbpedia.org/resource/Mạng_ngữ_nghĩa\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/abstract Das Semantic Web erweitert das Web, um Daten zwischen Rechnern einfacher austauschbar und für sie einfacher verwertbar zu machen; so kann beispielsweise das Wort „Bremen“ in einem Webdokument um die Information ergänzt werden, ob hier der Begriff des Schiffs-, Familien- oder Stadtnamens gemeint ist. Diese zusätzlichen Informationen explizieren die sonst nur unstrukturiert vorkommenden Daten. Zur Realisierung dienen Standards zur Veröffentlichung und Nutzung maschinenlesbarer Daten (insbesondere RDF).Während Menschen solche Informationen aus dem gegebenen Kontext schließen können (aus dem Gesamttext, über die Art der Publikation oder der Rubrik in selbiger, Bilder etc.) und derartige Verknüpfungen unbewusst aufbauen, muss Maschinen dieser Kontext erst beigebracht werden; hierzu werden die Inhalte mit weiterführenden Informationen verknüpft. Das Semantic Web beschreibt dazu konzeptionell einen „Giant Global Graph“ (engl. ‚gigantischer globaler Graph‘). Dabei werden sämtliche Dinge von Interesse identifiziert und mit einer eindeutigen Adresse versehen als Knoten angelegt, die wiederum durch Kanten (ebenfalls jeweils eindeutig benannt) miteinander verbunden sind. Einzelne Dokumente im Web beschreiben dann eine Reihe von Kanten, und die Gesamtheit all dieser Kanten entspricht dem globalen Graphen.\n", + "http://dbpedia.org/resource/Semantic_Web http://www.w3.org/2000/01/rdf-schema#comment Semantic Web (sieci semantyczne) – projekt, który ma przyczynić się do utworzenia i rozpowszechnienia standardów opisywania treści w Internecie w sposób, który umożliwi maszynom i programom (np. tzw. agentom) przetwarzanie informacji w sposób odpowiedni do ich znaczenia. Wśród standardów sieci semantycznych znajdują się m.in. OWL, RDF, RDF Schema (inaczej RDFS). Znaczenia zasobów informacyjnych określa się za pomocą tzw. ontologii.\n", + "http://dbpedia.org/resource/Semantic_Web http://dbpedia.org/ontology/wikiPageWikiLink http://dbpedia.org/resource/Nextbio\n" ] } ], @@ -531,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "id": "vhpFtnqF57x9" }, @@ -552,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "id": "66dQYVV46BLH" }, @@ -576,24 +593,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { - "id": "G-zlPMs36LF3", - "outputId": "f3f5b261-cb06-4640-e15e-94cb221a43c7", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "G-zlPMs36LF3", + "outputId": "db3d3e78-fd84-4f55-e56d-be28dd990191" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - ")>" + ")>" ] }, + "execution_count": 7, "metadata": {}, - "execution_count": 6 + "output_type": "execute_result" } ], "source": [ @@ -617,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "id": "XXVr_m726aGX" }, @@ -639,18 +656,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { - "id": "VBiHxQ8I6ckC", - "outputId": "2be09542-6dc2-4f6d-e3ce-528d206213aa", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "VBiHxQ8I6ckC", + "outputId": "4bad19d3-f29a-4740-c0d3-e1278f9156a6" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "@prefix foaf: .\n", "@prefix xsd: .\n", @@ -677,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "id": "g9y5vAR96m5J" }, @@ -694,30 +711,30 @@ "id": "KnQifktFAxHx" }, "source": [ - "# Create a Temporary ArangoDB Cloud Instance" + "# Setup ArangoDB" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ETS8l_NSAv0F", - "outputId": "65dea44e-200b-4b35-a70f-ff165148fb61" + "outputId": "e4242d73-6443-4292-9977-c3e9670d17ee" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Log: requesting new credentials...\n", "Succcess: new credentials acquired\n", "{\n", - " \"dbName\": \"TUTxfevvzm7lvczk545y3p8v\",\n", - " \"username\": \"TUT6evhebxvstnc8tazb033l7\",\n", - " \"password\": \"TUThxrcxgshd2itj4izw07l9l\",\n", + " \"dbName\": \"TUTphtgz1t47nl7cpqy3mym\",\n", + " \"username\": \"TUTj7a06zoicmtl7z0v36ncp\",\n", + " \"password\": \"TUTzfqnb7rmud8seknasx1nnr\",\n", " \"hostname\": \"tutorials.arangodb.cloud\",\n", " \"port\": 8529,\n", " \"url\": \"https://tutorials.arangodb.cloud:8529\"\n", @@ -731,139 +748,63 @@ "print(json.dumps(con, indent=2))\n", "\n", "# Connect to the db via the python-arango driver\n", - "db = ArangoClient(hosts=con[\"url\"]).db(con[\"dbName\"], con[\"username\"], con[\"password\"], verify=True)" + "db = ArangoClient(hosts=con[\"url\"]).db(\n", + " con[\"dbName\"],\n", + " con[\"username\"],\n", + " con[\"password\"],\n", + " verify=True\n", + " )" ] }, { "cell_type": "markdown", "metadata": { - "id": "7y81WHO8eG8_" + "id": "QfE_tKxneG9A" }, "source": [ - "# Data Import" + "# Instantiate ArangoRDF" ] }, { "cell_type": "markdown", "metadata": { - "id": "BM0iRYPDeG8_" + "id": "kGfhzPT9eG9A" }, "source": [ - "For demo purposes, we will be using the [ArangoDB Game Of Thrones Dataset](https://github.com/arangodb/example-datasets/tree/master/GameOfThrones)." + "Connect ArangoRDF to our temporary ArangoDB instance:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "7bgGJ3QkeG8_", - "outputId": "364c875a-7db6-42ea-9c18-96f6fb00748c" + "id": "oG496kBeeG9A", + "outputId": "ca895acc-a9ad-4287-8364-7591618b8b5b" }, "outputs": [ { + "name": "stderr", "output_type": "stream", - "name": "stdout", "text": [ - "\u001b[0m2023-12-20T15:45:49Z [621] INFO [05c30] {restore} Connected to ArangoDB 'http+ssl://tutorials.arangodb.cloud:8529'\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:49Z [621] INFO [abeb4] {restore} Database name in source dump is '_system'\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:49Z [621] INFO [9b414] {restore} # Re-creating document collection 'Characters'...\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:53Z [621] INFO [9b414] {restore} # Re-creating document collection 'Locations'...\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:57Z [621] INFO [9b414] {restore} # Re-creating document collection 'Traits'...\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:57Z [621] INFO [9b414] {restore} # Re-creating edge collection 'ChildOf'...\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:57Z [621] INFO [f723c] {restore} # Creating views...\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [6d69f] {restore} # Dispatched 4 job(s), using 2 worker(s)\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [94913] {restore} # Loading data into document collection 'Locations', data size: 361 byte(s)\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [94913] {restore} # Loading data into document collection 'Characters', data size: 1277 byte(s)\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [6ae09] {restore} # Successfully restored document collection 'Characters'\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [94913] {restore} # Loading data into document collection 'Traits', data size: 444 byte(s)\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [6ae09] {restore} # Successfully restored document collection 'Locations'\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [94913] {restore} # Loading data into edge collection 'ChildOf', data size: 399 byte(s)\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [6ae09] {restore} # Successfully restored document collection 'Traits'\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [6ae09] {restore} # Successfully restored edge collection 'ChildOf'\n", - "\u001b[0m\u001b[0m2023-12-20T15:45:58Z [621] INFO [a66e1] {restore} Processed 4 collection(s) in 8.906876 s, read 10815 byte(s) from datafiles, sent 4 data batch(es) of 10811 byte(s) total size\n", - "\u001b[0m" + "[2024/01/23 00:51:08 +0000] [252] [INFO] - arango_rdf: Instantiated ArangoRDF with database 'TUTphtgz1t47nl7cpqy3mym'\n", + "INFO:arango_rdf:Instantiated ArangoRDF with database 'TUTphtgz1t47nl7cpqy3mym'\n" ] } ], "source": [ - "!chmod -R 755 ArangoRDF/\n", - "!./ArangoRDF/tests/tools/arangorestore -c none --server.endpoint http+ssl://{con[\"hostname\"]}:{con[\"port\"]} --server.username {con[\"username\"]} --server.database {con[\"dbName\"]} --server.password {con[\"password\"]} --replication-factor 3 --input-directory \"ArangoRDF/tests/data/adb/got_dump\"" + "adbrdf = ArangoRDF(db)" ] }, { - "cell_type": "code", - "execution_count": 12, + "cell_type": "markdown", "metadata": { - "id": "IkWQ9W4UZcIz" + "id": "znQCjOwt7zBz" }, - "outputs": [], "source": [ - "if not db.has_graph(\"GameOfThrones\"):\n", - " db.create_graph(\n", - " \"GameOfThrones\",\n", - " edge_definitions=[\n", - " {\n", - " \"edge_collection\": \"ChildOf\",\n", - " \"from_vertex_collections\": [\"Characters\"],\n", - " \"to_vertex_collections\": [\"Characters\"],\n", - " },\n", - " ],\n", - " orphan_collections=[\"Traits\", \"Locations\"],\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QfE_tKxneG9A" - }, - "source": [ - "# Instantiate ArangoRDF" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kGfhzPT9eG9A" - }, - "source": [ - "Connect ArangoRDF to our temporary ArangoDB cluster:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oG496kBeeG9A", - "outputId": "1349178c-54df-4cdf-9d7c-55bd27e49b29" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[2023/12/20 15:45:58 +0000] [306] [INFO] - arango_rdf: Instantiated ArangoRDF with database 'TUTxfevvzm7lvczk545y3p8v'\n", - "INFO:arango_rdf:Instantiated ArangoRDF with database 'TUTxfevvzm7lvczk545y3p8v'\n" - ] - } - ], - "source": [ - "adbrdf = ArangoRDF(db)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "znQCjOwt7zBz" - }, - "source": [ - "# RDF to ArangoDB" + "# RDF to ArangoDB" ] }, { @@ -872,7 +813,8 @@ "id": "0qry3Bcy-160" }, "source": [ - "#### RPT vs PGT" + "#### ❗ RPT vs PGT ❗\n", + "\n" ] }, { @@ -881,16 +823,19 @@ "id": "0ONWNS6t8x7A" }, "source": [ - "RDF-to-ArangoDB functionality has been implemented using concepts described in the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781).\n", + "ArangoRDF's RDF-to-ArangoDB functionality has been implemented using concepts described in the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781).\n", "\n", - "In other words, ArangoRDF offers 2 RDF-to-ArangoDB transformation methods:\n", + "**TL;DR**:\n", "\n", - "1. RDF-topology Preserving Transformation (RPT): `ArangoRDF.rdf_to_arangodb_by_rpt()`\n", - "2. Property Graph Transformation (PGT): `ArangoRDF.rdf_to_arangodb_by_pgt()`\n", + "ArangoRDF has 2 RDF-to-ArangoDB transformation methods:\n", + "\n", + "1. **RDF-topology Preserving Transformation** (RPT): `ArangoRDF.rdf_to_arangodb_by_rpt()`\n", + "\n", + "2. **Property Graph Transformation** (PGT): `ArangoRDF.rdf_to_arangodb_by_pgt()`\n", "\n", "RPT preserves the RDF Graph structure by transforming each RDF Statement into an ArangoDB Edge.\n", "\n", - "PGT on the other hand ensures that Datatype Property Statements are mapped as ArangoDB Document Properties." + "PGT ensures that Datatype Property Statements (i.e Literal Statements) are mapped as ArangoDB Document Properties." ] }, { @@ -919,4601 +864,53527 @@ "id": "mRutdKii-Pk5" }, "source": [ - "#### Simple RPT & PGT Examples" + "#### Cases" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2PWrF6C5VByv" + }, + "source": [ + "RDF Graph Cases taken from the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { - "id": "cy_BWXK2AX5n", - "outputId": "78bb23af-af29-45f2-8cc2-ff47d05f6a0c", "colab": { "base_uri": "https://localhost:8080/", - "height": 82, + "height": 134, "referenced_widgets": [ - "b7fc8abdf6814a58bd2fa09ae4421720", - "8392609e27f64746bd7cb584b43c9872", - "01f90b74e45242d6b630726096ced105", - "070a0cf984774302b8afaf5cf0a321bd", - "5a4cf49e7d584df18700cbb9b9415b4b", - "2761dd724d38425b8595176fff7853b5" + "fc377f11c10c4d968c8a6caac22e0012", + "06aca56f72fb4329b49cfcc532ac8116", + "358dc9cbaa2f47f6aa3b14963b022222", + "fb6fc4469af9424c9c8eaaec53ddf478", + "2ddfd1e64d4747f8b3cc576d462d5f56", + "c1a0f2be84cd45749b0043f6510a4bfa", + "eee2941003754994b27156607ed4d79a", + "1d6d26d7b5a642529791ac860d335e29", + "999885ce87084d309d1c8c0972124094", + "d867c72e97bd48b686aac05d789729fe", + "e28d9f03ff10486fbe6f47ea396bc610", + "2cd5ecb36d81498595f558f842e23c05", + "9b60d80acd1140eb91da4ee24e3aec65", + "edc4bc9e70c3451299599dc199951288", + "407cfb7d093540daa3e77ed151028666", + "e849e373b5fd4798b5efae255bba65e7", + "ecf4cf0278e4442188384c7e7c2d7e56", + "4353e4aefa3b4d9a9d57249b27df4932", + "6df2082e6f484dc79472fccacc27c996", + "0d86bf821be447f4ab655cd136af247d", + "1ba7a0ca8b0646b8a5d9cb4ec400e03b", + "024dbdf4ef6d411f8f8a6e25603c68a7", + "3675e6b10c0748e387629f471847295a", + "ac58fe66dce145f0be7f770a26039acf" ] - } + }, + "id": "cy_BWXK2AX5n", + "outputId": "2ace5ff0-1603-4719-fe13-dcace5f832bf" }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "Output()" - ], "application/vnd.jupyter.widget-view+json": { + "model_id": "fc377f11c10c4d968c8a6caac22e0012", "version_major": 2, - "version_minor": 0, - "model_id": "b7fc8abdf6814a58bd2fa09ae4421720" - } + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [], "text/html": [ "
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-              ""
+              "\n"
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           "metadata": {},
-          "execution_count": 14
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-      "source": [
-        "# Case 1: Standard RDF statement\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "\n",
-        "ex:alice a ex:Person .\n",
-        "ex:bob a ex:Person .\n",
-        "ex:alice ex:meets ex:bob .\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
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-              ""
+              "\n"
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           },
           "metadata": {},
-          "execution_count": 15
-        }
-      ],
-      "source": [
-        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
-        "# Case 2.1: Predicate as subject and literal as object\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdfs:  .\n",
-        "\n",
-        "ex:Sam ex:mentor ex:Lee .\n",
-        "ex:mentor rdfs:label \"project supervisor\" .\n",
-        "ex:mentor ex:name \"mentor's name\" .\n",
-        "\n",
-        "ex:Sam a ex:Person .\n",
-        "ex:Lee a ex:Person .\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
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           "data": {
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           "metadata": {},
-          "execution_count": 16
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-      "source": [
-        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
-        "# Case 2.2: Predicate as subject and RDF resource as object\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "\n",
-        "ex:Martin ex:mentorJoe ex:Joe.\n",
-        "ex:mentorJoe ex:alias ex:teacher .\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
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-      "execution_count": 17,
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       "source": [
-        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
-        "# Case 2.3: Predicate as subject and RDF property as object - rdfs:subPropertyOf\n",
+        "# Case 1: Standard RDF statement\n",
         "\n",
         "data = \"\"\"\n",
-        "@prefix rdfs:  .\n",
         "@prefix ex:  .\n",
         "\n",
-        "ex:Jan a ex:Person .\n",
-        "ex:Leo a ex:Person .\n",
-        "ex:Jan ex:supervise ex:Leo .\n",
-        "\n",
-        "ex:supervise rdfs:subPropertyOf ex:administer .\n",
+        "ex:alice a ex:Person .\n",
+        "ex:bob a ex:Person .\n",
+        "ex:alice ex:meets ex:bob .\n",
         "\"\"\"\n",
         "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 14,
       "metadata": {
-        "id": "WraNcreKcJ35",
-        "outputId": "80a19c1b-4de4-4cd1-b315-355058ca9b42",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 82,
+          "height": 150,
           "referenced_widgets": [
-            "14043b1e5a024c2c9de96dc1ef80cc69",
-            "c9bc8a0c2e2948349479061cb80f0634",
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-            "378aa87bb1204456b3c458d5d7eb2451",
-            "8b9f1ace152146efb018a69ffa44a923",
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+            "e8be2a32f6d341998985b2c1f4e3d9c9",
+            "95d6f533e8a240c3b0d4aad2e9580335"
           ]
-        }
+        },
+        "id": "9BFNRAzLDmzU",
+        "outputId": "5038bf6e-b8be-410f-afe4-3dcdca407898"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "34082cf6eb994d0e8dc1c5a32103e6a8",
               "version_major": 2,
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-              "model_id": "14043b1e5a024c2c9de96dc1ef80cc69"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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-            ]
-          },
-          "metadata": {}
-        },
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-            "text/plain": [
-              "Output()"
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-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "645c43cc66e74996980bf761878dc058"
-            }
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
-              "
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+              "
\n",
+              "
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+            ],
+            "text/plain": [
+              "\n"
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           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "39bbccb261f74e9696665119136f35ee",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "8b9f1ace152146efb018a69ffa44a923"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 18
-        }
-      ],
-      "source": [
-        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
-        "# Case 2.4: Predicate as subject and RDF class as object - rdf:type\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf: .\n",
-        "\n",
-        "ex:Tom ex:friend ex:Chris .\n",
-        "ex:friend rdf:type ex:relation .\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "id": "00ooim92Ekxv",
-        "outputId": "f5ef499c-da4f-4037-d595-bb889dbc3f72",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 98,
-          "referenced_widgets": [
-            "ad273e4bc37141e894b7aa9b3944c2ad",
-            "90ded7fa35bf4d7b9158a6245254bd61",
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-            "fb5ba4b4ac504bbcae9d2f87e29fae08",
-            "187eb516a1ae418b9f5599e5388a6c54",
-            "f57975d49e7a49d0a7c4bac35af2e707"
-          ]
-        }
-      },
-      "outputs": [
+          "output_type": "display_data"
+        },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "03740e7e6199495cbaebf039d6fea20a",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ad273e4bc37141e894b7aa9b3944c2ad"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7f35f5dbf07e47f78d51f42f62c42258",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "bbd05eae3a9c4a87abfb89b2fa305df1"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "82621e9cb2ba47baafa2c1580d91740b",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "187eb516a1ae418b9f5599e5388a6c54"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 19
-        }
-      ],
-      "source": [
-        "# Case 3: Data types and language tags\n",
-        "# Case 3.1: Datatype property statements with different data types of the literal objects\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix xsd:  .\n",
-        "\n",
-        "ex:book ex:publish_date \"1963-03-22\" .\n",
-        "ex:book ex:pages \"100\"^^xsd:integer .\n",
-        "ex:book ex:cover 20 .\n",
-        "ex:book ex:index \"55\" .\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {
-        "id": "UmIn_SZWccN2",
-        "outputId": "af2e57d7-a7b8-4c2a-a1ab-9e543c68d6a6",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 98,
-          "referenced_widgets": [
-            "e6cabf8b877c4a529dc06c11d2799dc7",
-            "4dab918581ac47f19d87c6ed4b5510d6",
-            "5d24e5e2c2c94bd7be68cab18ed0d54a",
-            "1dbd5f86325c4634a3646a811d0c5b79",
-            "9d7882a570e544f6984d5cd65db8e30f",
-            "917bdaff66d44d7584c83a15c825b348"
-          ]
-        }
-      },
-      "outputs": [
+          "output_type": "display_data"
+        },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a2b0b7c7cc6a4057b8743464c0fb33a8",
               "version_major": 2,
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-              "model_id": "e6cabf8b877c4a529dc06c11d2799dc7"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "5d24e5e2c2c94bd7be68cab18ed0d54a"
-            }
+            "text/plain": [
+              "\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
-            "text/html": [
-              "
\n"
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d66eb2a7b7f943e2b9bc8f3c3567da79",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n"
             ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "9d7882a570e544f6984d5cd65db8e30f"
-            }
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
-              "
\n"
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b5aaff1d7205458b8a9ef5457d2e3600",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
-              ""
+              "Output()"
             ]
           },
           "metadata": {},
-          "execution_count": 20
-        }
-      ],
-      "source": [
-        "# Case 3: Data types and language tags\n",
-        "# Case 3.2: Datatype property statements with different language tags of the literal objects\n",
-        "# NOTE: PGT will currently discard the language tags!\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "\n",
-        "ex:book ex:title \"Book\"@en.\n",
-        "ex:book ex:title \"Bog\"@da.\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "id": "SQaaqperccbA",
-        "outputId": "48c00702-8148-4f5f-b2ab-18dee10e9e2e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 82,
-          "referenced_widgets": [
-            "eb7475d731a944c9b81f63b8590ff3f5",
-            "4b99b0630ce440dfb3de98cbea514472",
-            "39975c741ff64d7daa6606fbbb56cb8f",
-            "970332934439402cbd8b34fa470e696a",
-            "19ffc23068b44fb791f18c3ca93f255c",
-            "c58529b8b756480fb8a7177b1b4f446c"
-          ]
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Output()"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "eb7475d731a944c9b81f63b8590ff3f5"
-            }
-          },
-          "metadata": {}
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "54182c5e13f84178b64673a1ddfc3a42",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "39975c741ff64d7daa6606fbbb56cb8f"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [],
-            "text/html": [
-              "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
+              "version_minor": 0
+            },
             "text/plain": [
               "Output()"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "19ffc23068b44fb791f18c3ca93f255c"
-            }
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 21
-        }
-      ],
-      "source": [
-        "# Case 4: RDF list\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "\n",
-        "ex:List1 ex:contents (\"one\" \"two\" \"three\").\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "id": "r0kHJogZFEKO",
-        "outputId": "9d11c893-78f8-46da-e46a-26a801e7bc2b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 66,
-          "referenced_widgets": [
-            "6b9f73dfe80f47459fb99e11ca099d45",
-            "20ffded7a8164953ac6427b81f48d700",
-            "f4604c4e93634d4eb397a73656b84428",
-            "a6aba994741c478db57553f48f0862e1",
-            "8e67325edc404ea0a3ea5831b402f2ba",
-            "257df207329b4db3a6dcfe3488f44707"
-          ]
-        }
-      },
-      "outputs": [
+          "output_type": "display_data"
+        },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
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               "version_major": 2,
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-              "model_id": "6b9f73dfe80f47459fb99e11ca099d45"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
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               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "f4604c4e93634d4eb397a73656b84428"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e8be2a32f6d341998985b2c1f4e3d9c9",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "8e67325edc404ea0a3ea5831b402f2ba"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              ""
-            ]
+            ],
+            "text/plain": []
           },
           "metadata": {},
-          "execution_count": 22
+          "output_type": "display_data"
         }
       ],
       "source": [
-        "# Case 5: Blank nodes\n",
+        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
+        "# Case 2.1: Predicate as subject and literal as object\n",
         "\n",
         "data = \"\"\"\n",
         "@prefix ex:  .\n",
+        "@prefix rdfs:  .\n",
         "\n",
-        "ex:bob ex:nationality _:c .\n",
-        "_:c ex:country \"Canada\" .\n",
-        "\"\"\"\n",
+        "ex:Sam ex:mentor ex:Lee .\n",
+        "ex:mentor rdfs:label \"project supervisor\" .\n",
+        "ex:mentor ex:name \"mentor's name\" .\n",
         "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
+        "ex:Sam a ex:Person .\n",
+        "ex:Lee a ex:Person .\n",
+        "\"\"\"\n",
         "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 15,
       "metadata": {
-        "id": "noAXcHOJFJvG",
-        "outputId": "f235d415-e325-434c-e4c4-09379cb4439a",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 82,
+          "height": 134,
           "referenced_widgets": [
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-            "18f971e8a5564de3a443aeee11506522",
-            "1d2224ef32a2471390016c487eebdc03"
+            "36998aa1db0d4404a02bef5abfa995c8",
+            "f529a9ce3a0a4fdf832eae87d164d458",
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+            "4f58d9bac09c404c9f2b1f7d6007109d",
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+            "324aaf266fbe4a73a48096790b2b3a8a",
+            "bcaf27bfd2b94b088aee76f8c1c0b394"
           ]
-        }
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+        "id": "E6t4VRcsD2m7",
+        "outputId": "40d573e1-c1fc-449c-88c7-4ad8e1c44f29"
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-          "output_type": "display_data",
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-            "text/plain": [
-              "Output()"
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+            "text/plain": [
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             "text/html": [
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-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
-              "
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+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "52cbbdbb898f48deae681ff01d1468c8",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "18f971e8a5564de3a443aeee11506522"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 23
-        }
-      ],
-      "source": [
-        "# Case 6: Named graphs\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "@prefix rdfs:  .\n",
-        "\n",
-        "ex:Monica ex:employer ex:ArangoDB .\n",
-        "\n",
-        "ex:Graph1 {\n",
-        "    ex:Monica a ex:Entity .\n",
-        "    ex:Management a ex:Skill .\n",
-        "    ex:Monica ex:name \"Monica\" .\n",
-        "    ex:Monica ex:homepage  .\n",
-        "    ex:Monica ex:hasSkill ex:Management .\n",
-        "}\n",
-        "\n",
-        "ex:Graph2 {\n",
-        "    ex:Programming a ex:Skill .\n",
-        "     a ex:Website .\n",
-        "    ex:Monica a ex:Person .\n",
-        "    ex:Person rdfs:subClassOf ex:Entity .\n",
-        "    ex:Monica ex:hasSkill ex:Programming .\n",
-        "}\n",
-        "\"\"\"\n",
-        "\n",
-        "cg = ConjunctiveGraph()\n",
-        "cg.parse(data=data, format='trig')\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", cg, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", cg, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "gtHKG7PiGyyF",
-        "outputId": "ab8631f6-a2f2-496f-ff39-d95b727d041e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 98,
-          "referenced_widgets": [
-            "3cf793a681e54b49af833c758869e646",
-            "fb292dca1c644e00be5203be17eb9b09",
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-            "da18f12264f94c8781507ed587f9fb53",
-            "024794f3124547ec9826e3a94407a545",
-            "74c1efb3701d4a0983f6c5eea22402a6"
-          ]
-        }
-      },
-      "outputs": [
+          "output_type": "display_data"
+        },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
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-              "model_id": "3cf793a681e54b49af833c758869e646"
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+              "version_minor": 0
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+            "text/plain": [
+              "Output()"
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           },
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+          "metadata": {},
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-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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+            "text/plain": []
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-          "metadata": {}
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         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
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             "application/vnd.jupyter.widget-view+json": {
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-              "model_id": "1edeb3a3438645c790a213a859131f95"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5c8ea494175c47e1b55304e2730df295",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "024794f3124547ec9826e3a94407a545"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 24
-        }
-      ],
-      "source": [
-        "# Case 7: Multiple types for resources - rdf:type\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix :  .\n",
-        "@prefix rdfs:  .\n",
-        "@prefix adb:  .\n",
-        "@prefix owl:  .\n",
-        "\n",
-        ":alice a :Arson .\n",
-        ":alice a :Author .\n",
-        "\n",
-        ":Zenkey rdfs:subClassOf :Zebra .\n",
-        ":Zenkey rdfs:subClassOf :Donkey .\n",
-        ":Donkey rdfs:subClassOf :Animal .\n",
-        ":Zebra rdfs:subClassOf :Animal .\n",
-        ":Human rdfs:subClassOf :Animal .\n",
-        ":Animal rdfs:subClassOf :LivingThing .\n",
-        ":LivingThing rdfs:subClassOf :Thing .\n",
-        ":Thing rdfs:subClassOf :Object .\n",
-        "\n",
-        ":charlie a :LivingThing .\n",
-        ":charlie a :Animal .\n",
-        ":charlie a :Zenkey .\n",
-        "\n",
-        ":marty a :LivingThing .\n",
-        ":marty a :Animal .\n",
-        ":marty a :Human .\n",
-        "\n",
-        ":john a :Singer .\n",
-        ":john a :Writer .\n",
-        ":john a :Guitarist .\n",
-        ":john adb:collection \"Artist\" .\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7ZeZNno5k3XW"
-      },
-      "source": [
-        "Cases 8 - 15: RDF-Star\n",
-        "\n",
-        "`rdflib` has yet to introduce support for [Quoted Triples](https://www.w3.org/TR/rdf12-concepts/#dfn-quoted-triple), so ArangoRDF's support for RDF-star is based on [Triple Reification](https://www.w3.org/wiki/RdfReification)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 98,
-          "referenced_widgets": [
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-      "outputs": [
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-          "output_type": "display_data",
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-            "text/plain": [
-              "Output()"
-            ],
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+              "
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+              "
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+            "text/plain": [
+              "\n"
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+              "version_minor": 0
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+        {
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+              "
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+              "
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+            "text/plain": [
+              "\n"
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               "Output()"
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+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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-          "output_type": "display_data",
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           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 25
-        }
-      ],
-      "source": [
-        "# Case 8:  Embedded object property statement in subject position\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "<< ex:alice ex:likes ex:bob >> ex:certainty 0.5 .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:alice;\n",
-        "    rdf:predicate ex:likes;\n",
-        "    rdf:object ex:bob ;\n",
-        "    ex:certainty 0.5 .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "KAs-MpmAp8_c",
-        "outputId": "ee68d39c-3b2c-45c0-9684-8d90df1e5e40",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 66,
-          "referenced_widgets": [
-            "ae8d8107b06e44ca93dea02970a9f249",
-            "b0921c20c9064e24af4cc089d8391449"
-          ]
-        }
-      },
-      "outputs": [
+          "output_type": "display_data"
+        },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "442ab928de054d89834465fa7d3f6c90",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
             ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5474d353569b4aa99ca7b0cfba1a3a45",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ae8d8107b06e44ca93dea02970a9f249"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "324aaf266fbe4a73a48096790b2b3a8a",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
-              ""
+              "Output()"
             ]
           },
           "metadata": {},
-          "execution_count": 26
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
-        "# Case 9: Embedded datatype property statement in subject position\n",
-        "# Note: PGT does not support this case\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "<< ex:Mark ex:age 28 >> ex:certainty 1 .\n",
-        "\"\"\"\n",
+        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
+        "# Case 2.2: Predicate as subject and RDF resource as object\n",
         "\n",
         "data = \"\"\"\n",
         "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:Mark;\n",
-        "    rdf:predicate ex:age;\n",
-        "    rdf:object 28 ;\n",
-        "    ex:certainty 1 .\n",
         "\n",
+        "ex:Martin ex:mentorJoe ex:Joe.\n",
+        "ex:mentorJoe ex:alias ex:teacher .\n",
         "\"\"\"\n",
         "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)"
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 98,
+          "height": 134,
           "referenced_widgets": [
-            "37b3248d6772424eb60a7e971ecd02b1",
-            "f634630cea00413faaa7b0411b55f795",
-            "7ab128ea18a746f4969526ebb3171a27",
-            "4e8d612cd3af44248421dc9dce4ae418",
-            "154e7c01b36e4a6f8f6b6c95ae930fbe",
-            "da3673541a9a43d790679a1cd27b1e35"
+            "d09c5ec60dce44c1a554ab5708e1a06a",
+            "4e9d7516d4b447e9af814d0ef8e1a33e",
+            "299cedcd596747f7a8837d2ef1a25f5c",
+            "2f3cc4e6401444d1a3154659afc9efab",
+            "5498eaee5d4147e7837cb6c6c563dfbc",
+            "a8d4da8fc9f9471e9bc5d328b4ca8d90",
+            "62199c1525d34669b32ec33744030077",
+            "554c15b9173641b199741a03138c3ec5",
+            "17719aef6c534bddabefe7a8842e743b",
+            "fbc6745df8724e9e9379e3ab3b07b2c5",
+            "ccbc35240b3648ae9b30318be78f7b79",
+            "f3bb03a8be4645cfa5f39fecdee44057",
+            "b81d313da9914c0dbc9401bb61e36cd2",
+            "306ec886b3264c6fa6df970754f7f603",
+            "e9c373c0a0b14537bcca1d43a4aa7d58",
+            "6bd89d16c4d649538dc7213f8abc7aed",
+            "06838a4ca6e3487abf98d48ac909f144",
+            "746745f6f03543e08fd4429cc5cc900f",
+            "1656f5d65cf74bc09f36c42d6805e634",
+            "db682b0171354a50bc8e7ae48d5d49f1",
+            "e108a1590816436995542b612fedce0c",
+            "f777d49fe0d0408f8df7051c16a726e5",
+            "094c0ec357ef44c395c39ae90dd66e7a",
+            "f6ddbd6c083449d1b675b1829c5b682f"
           ]
         },
-        "id": "_ZWrGS9Uqoc1",
-        "outputId": "38d7ce36-5ef3-48fc-e4a6-0aeccba1faae"
+        "id": "NEDGhDfzEEhg",
+        "outputId": "e3e7726e-f2fa-44c0-9f4a-0923128887a3"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d09c5ec60dce44c1a554ab5708e1a06a",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "37b3248d6772424eb60a7e971ecd02b1"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7ab128ea18a746f4969526ebb3171a27"
-            }
+            "text/plain": [
+              "\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
-            "text/html": [
-              "
\n"
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "299cedcd596747f7a8837d2ef1a25f5c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n"
             ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "154e7c01b36e4a6f8f6b6c95ae930fbe"
-            }
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
-              "
\n"
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5498eaee5d4147e7837cb6c6c563dfbc",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
-              ""
+              "Output()"
             ]
           },
           "metadata": {},
-          "execution_count": 27
-        }
-      ],
-      "source": [
-        "# Case 10: Embedded object property statement in object position\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "ex:bobshomepage ex:source << ex:mainPage ex:writer ex:alice >> .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "_:x a rdf:Statement;\n",
-        "    rdf:subject ex:mainPage;\n",
-        "    rdf:predicate ex:writer;\n",
-        "    rdf:object ex:alice .\n",
-        "\n",
-        "ex:bobshomepage ex:source _:x .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 82,
-          "referenced_widgets": [
-            "80bf39d6f8d24250b39ac0a599968566",
-            "48fc42804d1f4c39ab5c121c804c15db",
-            "59396ffe39ea4b93beffcaf1107a99e7",
-            "2554a40b9c0145cabf9b130ce942e09e",
-            "024fb25b58194b49ad15c2ad689300fc",
-            "7f8f299bbac840688fb806012e52afb2"
-          ]
+          "output_type": "display_data"
         },
-        "id": "E_iK33XDSiml",
-        "outputId": "a6b46de1-b08f-4e4c-973a-da1eca899e0a"
-      },
-      "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n"
             ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "62199c1525d34669b32ec33744030077",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "80bf39d6f8d24250b39ac0a599968566"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "17719aef6c534bddabefe7a8842e743b",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "59396ffe39ea4b93beffcaf1107a99e7"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ccbc35240b3648ae9b30318be78f7b79",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "024fb25b58194b49ad15c2ad689300fc"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 28
-        }
-      ],
-      "source": [
-        "# Case 11: Embedded object property statement in subject position and non-literal object\n",
-        "# Case 11.1: Asserted statement with non-literal object\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "<< ex:mainPage ex:writer ex:alice >> ex:source ex:bobshomepage .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:mainPage;\n",
-        "    rdf:predicate ex:writer;\n",
-        "    rdf:object ex:alice ;\n",
-        "    ex:source ex:bobshomepage .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 98,
-          "referenced_widgets": [
-            "643e339e031842f0907dc830b6ee151a",
-            "f160e75392014b2c9ac8141dd6d429a3",
-            "e9ce05ba301a42029120497a2856ce2c",
-            "2fc5d453ea01439589427d19fcb8a908",
-            "e4b9fdeb356d4317900435592a2cce8f",
-            "2e1a3dad266c4481a88f8e97cf89a1a6"
-          ]
+          "output_type": "display_data"
         },
-        "id": "0oZbDeLeS6ll",
-        "outputId": "95aa2c20-d50c-4b4c-b64e-c7caeb0a2ffc"
-      },
-      "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b81d313da9914c0dbc9401bb61e36cd2",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "643e339e031842f0907dc830b6ee151a"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e9c373c0a0b14537bcca1d43a4aa7d58",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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             ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "model_id": "06838a4ca6e3487abf98d48ac909f144",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e9ce05ba301a42029120497a2856ce2c"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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             ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
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-              "model_id": "e4b9fdeb356d4317900435592a2cce8f"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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-            ]
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+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "094c0ec357ef44c395c39ae90dd66e7a",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
-              ""
+              "Output()"
             ]
           },
           "metadata": {},
-          "execution_count": 29
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
-        "# Case 11: Embedded object property statement in subject position and non-literal object\n",
-        "# Case 11.2: Asserted statement with non-literal object that appears in another asserted statement\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "ex:alex ex:age 25 .\n",
-        "<< ex:alice ex:friend ex:bob >> ex:mentionedBy ex:alex .\n",
-        "\"\"\"\n",
+        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
+        "# Case 2.3: Predicate as subject and RDF property as object - rdfs:subPropertyOf\n",
         "\n",
         "data = \"\"\"\n",
+        "@prefix rdfs:  .\n",
         "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "ex:alex ex:age 25 .\n",
         "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:alice;\n",
-        "    rdf:predicate ex:friend;\n",
-        "    rdf:object ex:bob ;\n",
-        "    ex:mentionedBy ex:alex .\n",
+        "ex:Jan a ex:Person .\n",
+        "ex:Leo a ex:Person .\n",
+        "ex:Jan ex:supervise ex:Leo .\n",
         "\n",
+        "ex:supervise rdfs:subPropertyOf ex:administer .\n",
         "\"\"\"\n",
         "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": 17,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 82,
+          "height": 134,
           "referenced_widgets": [
-            "cca8a65fe7964b40b1b3903fd13a9ccc",
-            "9ad6a405ec67438d83837fa952e92912",
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-            "112e6b19eccb4d7d926a22e7ee4711ad",
-            "0d158161a3864a12b3bf3e5606653884"
+            "4cb8bd60a6174fdaa24c09f9a852fa18",
+            "14139b121edc4b42bf05b1bf35f232ef",
+            "59c0abf74b4d4276ba5bc305f28d26a8",
+            "5382e4076e9d4b058082df8c722e0668",
+            "5eb7089127fb481a93e44a1468c01914",
+            "260648c894104cb092ede5d76ef44111",
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+            "56a5349593d642ebaf100d6a50abf506",
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-        "id": "woNfHiZ5S__t",
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+        "id": "WraNcreKcJ35",
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
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               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "cca8a65fe7964b40b1b3903fd13a9ccc"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
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           },
-          "metadata": {}
+          "metadata": {},
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-          "output_type": "display_data",
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-            "text/plain": [],
             "text/html": [
               "
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-            ]
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-          "data": {
-            "text/plain": [
-              "Output()"
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-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "1521b81d67b44c878d14c9fca516442f"
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
-              "
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+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
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           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "59c0abf74b4d4276ba5bc305f28d26a8",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "112e6b19eccb4d7d926a22e7ee4711ad"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 30
-        }
-      ],
-      "source": [
-        "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n",
-        "# Case 12.1: Asserted statement with rdf:type as predicate\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "<< ex:mainPage ex:writer ex:alice >> rdf:type ex:bobshomepage .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:mainPage;\n",
-        "    rdf:predicate ex:writer;\n",
-        "    rdf:object ex:alice ;\n",
-        "    rdf:type ex:bobshomepage .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 0,
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-            "64953fecd79344a288fe45e239340586",
-            "37aa954141534869942a355e348d1bc5",
-            "8c3d7146d54949aa8fe26416f332a39e"
-          ]
+          "output_type": "display_data"
         },
-        "id": "lbbdb2lwS_2M",
-        "outputId": "d529b9db-32ee-45f7-9d5b-ac07875c4d18"
-      },
-      "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
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               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "943dbdccdee14f9181cd6ddb5649ba6e"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e97722a842234f0da935308222ef07af",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e9532a22334d4cb58d467aea26a078bd"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ee08f8a0423141508d6163d09448e7a4",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "37aa954141534869942a355e348d1bc5"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 31
-        }
-      ],
-      "source": [
-        "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n",
-        "# Case 12.2: Embedded statement with rdf:type as predicate\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "<< ex:lara rdf:type ex:writer >> ex:owner ex:journal .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:lara;\n",
-        "    rdf:predicate rdf:type;\n",
-        "    rdf:object ex:writer ;\n",
-        "    ex:owner ex:journal .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "id": "4TwIzKZ4S_tN",
-        "outputId": "a82197b5-247c-4f98-977a-a68f5cf6c1bb",
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-          "referenced_widgets": [
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-            "6fee3fa7e01e45a89733e4ac0f084a27",
-            "4853f718f1d644ba8c2831dde6ecf641",
-            "fabb5e3ab845455c85f9d3d6e1345bdc"
-          ]
-        }
-      },
-      "outputs": [
+          "output_type": "display_data"
+        },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
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               "version_major": 2,
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-              "model_id": "7a195dd506874f14b6ea7fdcb3e17d5c"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
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               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "0e1a16d9193541d6828cc29365915a45"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b7c35383db9149c0bd7bc1b6c845e7c7",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4853f718f1d644ba8c2831dde6ecf641"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "31cdeffecb6e4de199d9b8925ec7a567",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
-              ""
+              "Output()"
             ]
           },
           "metadata": {},
-          "execution_count": 32
-        }
-      ],
-      "source": [
-        "# Case 13: Double nested RDF-star statement in subject position\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "<< << ex:Steve ex:position ex:CEO >> ex:mentionedBy ex:book >> ex:source ex:Journal .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "_:x a rdf:Statement;\n",
-        "    rdf:subject ex:Steve;\n",
-        "    rdf:predicate ex:position;\n",
-        "    rdf:object ex:CEO .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject _:x;\n",
-        "    rdf:predicate ex:mentionedBy;\n",
-        "    rdf:object ex:book;\n",
-        "    ex:source ex:Journal .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 0,
-          "referenced_widgets": [
-            "4b38704a488949bcb63948eef58eb730",
-            "51b70484aef7409eb48c1ea0c17250dd",
-            "57b393748251483d8c654588ae791281",
-            "e848cf868d484e5f9481f558c2cb3258",
-            "c2a362f33de74b39826d2fd604cf0d31",
-            "4e7fd35dbbbb4f9daacf07b88db681a0"
-          ]
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
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+              "
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-              "
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+              "
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+              "
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+            "text/plain": [
+              "\n"
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           "data": {
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-              "Output()"
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+              "Output()"
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           "data": {
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             "text/html": [
               "
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+            "text/plain": []
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
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               "version_major": 2,
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-              "model_id": "c2a362f33de74b39826d2fd604cf0d31"
-            }
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+            },
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+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a68461a127d2438fabf18b90f0e7a0f3",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
-              ""
+              "Output()"
             ]
           },
           "metadata": {},
-          "execution_count": 33
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
-        "# Case 14: Multi-valued properties\n",
-        "# Case 14.1: RDF statements with same subject and predicate and different objects\n",
+        "# Case 2: The predicate of an RDF statement is subject in another statement\n",
+        "# Case 2.4: Predicate as subject and RDF class as object - rdf:type\n",
         "\n",
         "data = \"\"\"\n",
         "@prefix ex:  .\n",
+        "@prefix rdf: .\n",
         "\n",
-        "ex:college_page ex:subject \"Info_Page\";\n",
-        "    ex:subject \"aau_page\" .\n",
+        "ex:Tom ex:friend ex:Chris .\n",
+        "ex:friend rdf:type ex:relation .\n",
         "\"\"\"\n",
         "\n",
         "g = Graph()\n",
         "g.parse(data=data)\n",
         "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)\n"
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
+      "execution_count": 18,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
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-        "id": "X_qBsxffS_br",
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+        "id": "00ooim92Ekxv",
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         {
-          "output_type": "display_data",
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-              "Output()"
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-              "
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+              "
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
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               "version_major": 2,
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-              "model_id": "c5014016a5d146e9890bf9e3c2d4ba66"
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+              "version_minor": 0
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+              "Output()"
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           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
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+              "
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+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 34
-        }
-      ],
-      "source": [
-        "# Case 14: Multi-valued properties\n",
-        "# Case 14.2: RDF-star statements with the same subject and predicate and different objects\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "<< ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 .\n",
-        "<< ex:Mary ex:likes ex:Matt >> ex:certainty 1 .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "@prefix xsd:  .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:Mary;\n",
-        "    rdf:predicate ex:likes;\n",
-        "    rdf:object ex:Matt ;\n",
-        "    ex:certainty 0.5 .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:Mary;\n",
-        "    rdf:predicate ex:likes;\n",
-        "    rdf:object ex:Matt ;\n",
-        "    ex:certainty 1 .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
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-            "4060b9b79c4e45ae9030259327cf4ebd"
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-        "id": "cD-S3cZ-S_Ta",
-        "outputId": "630ab417-6b8b-49e2-aa72-cc02dd141a9f"
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-      "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
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               "version_major": 2,
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-              "model_id": "549aa1199ec24346a2a3dbd9345538b5"
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
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               "version_major": 2,
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-              "model_id": "9df9935236354a1eb485cca94f84d49b"
-            }
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+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
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         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a5878d1b00ea420a80da1504a988cebd",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e812902851574e0399a9c8b9127a464d"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
             "text/plain": [
-              ""
+              "\n"
             ]
           },
           "metadata": {},
-          "execution_count": 35
-        }
-      ],
-      "source": [
-        "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
-        "\n",
-        "\"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "\n",
-        "<< ex:Mary ex:likes ex:Matt >> ex:certainty 0.5 .\n",
-        "<< ex:Mary ex:likes ex:Matt >> ex:source \"text\" .\n",
-        "\"\"\"\n",
-        "\n",
-        "data = \"\"\"\n",
-        "@prefix ex:  .\n",
-        "@prefix rdf:  .\n",
-        "@prefix xsd:  .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:Mary;\n",
-        "    rdf:predicate ex:likes;\n",
-        "    rdf:object ex:Matt;\n",
-        "    ex:certainty 0.5 .\n",
-        "\n",
-        "[] a rdf:Statement;\n",
-        "    rdf:subject ex:Mary;\n",
-        "    rdf:predicate ex:likes;\n",
-        "    rdf:object ex:Matt;\n",
-        "    ex:source \"text\" .\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0SWi4e3wIMtw"
-      },
-      "source": [
-        "#### RDF to ArangoDB w/ Graph Contextualization"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vec21mb9MkhR"
-      },
-      "source": [
-        "Contextualizing an RDF Graph within ArangoDB is a work-in-progress feature that attempts to enhance the Terminology Box of the original RDF Graph. This is done by:\n",
-        "\n",
-        "1.   Loading the OWL, RDF, and RDFS Ontologies as 3 sub-graphs via `ArangoRDF.load_meta_ontology()`\n",
-        "2.   Setting the `contextualize_graph` flag to `True` in any of the `rdf_to_arangodb` methods.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dUOCXzn5Owhj"
-      },
-      "source": [
-        "Enabling the `contextualize_graph` flag currently provides the following features:\n",
-        "\n",
-        "1.   Process RDF Predicates within the RDF Graph as their own ArangoDB Document, and cast a (predicate RDF.type RDF.Property) edge relationship into the ArangoDB graph for every RDF predicate used in the form (subject predicate object) within the RDF Graph.\n",
-        "\n",
-        "2. Provide RDFS.Domain & RDFS.Range Inference on all RDF Resources within the RDF Graph, so long that no RDF.Type statement already exists in RDF Graph for the given resource.\n",
-        "\n",
-        "3. Provide RDFS.Domain & RDFS.Range Introspection on all RDF Predicates within the RDF Graph, so long that no RDFS.Domain or RDFS.Range statement already exists for the given predicate."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "id": "P9oGi91RJbAI",
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+            "text/html": [
+              "
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+              "
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-              ""
+              "\n"
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           "metadata": {},
-          "execution_count": 36
-        }
-      ],
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-        "data = \"\"\"\n",
-        "PREFIX : \n",
-        "PREFIX rdf: \n",
-        "PREFIX rdfs: \n",
-        "PREFIX xsd: \n",
-        "\n",
-        ":The_Beatles      rdf:type  :Band .\n",
-        ":The_Beatles      :name     \"The Beatles\" .\n",
-        ":The_Beatles      :member   :John_Lennon .\n",
-        ":The_Beatles      :member   :Paul_McCartney .\n",
-        ":The_Beatles      :member   :Ringo_Starr .\n",
-        ":The_Beatles      :member   :George_Harrison .\n",
-        ":John_Lennon      rdf:type  :SoloArtist .\n",
-        ":Paul_McCartney   rdf:type  :SoloArtist .\n",
-        ":Ringo_Starr      rdf:type  :SoloArtist .\n",
-        ":George_Harrison  rdf:type  :SoloArtist .\n",
-        ":Please_Please_Me rdf:type  :Album .\n",
-        ":Please_Please_Me :name     \"Please Please Me\" .\n",
-        ":Please_Please_Me :date     \"1963-03-22\"^^xsd:date .\n",
-        ":Please_Please_Me :artist   :The_Beatles .\n",
-        ":Please_Please_Me :track    :Love_Me_Do .\n",
-        ":Love_Me_Do       rdf:type  :Song .\n",
-        ":Love_Me_Do       :name     \"Love Me Do\" .\n",
-        ":Love_Me_Do       :length   125 .\n",
-        ":Love_Me_Do       :writer   :John_Lennon .\n",
-        ":Love_Me_Do       :writer   :Paul_McCartney .\n",
-        "\n",
-        ":McCartney        rdf:type  :Album .\n",
-        ":McCartney        :name     \"McCartney\" .\n",
-        ":McCartney        :date     \"1970-04-17\"^^xsd:date .\n",
-        ":McCartney        :artist   :Paul_McCartney .\n",
-        "\n",
-        ":Imagine          rdf:type  :Album .\n",
-        ":Imagine          :name     \"Imagine\" .\n",
-        ":Imagine          :date     \"1971-10-11\"^^xsd:date .\n",
-        ":Imagine          :artist   :John_Lennon .\n",
-        "\"\"\"\n",
-        "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "g = adbrdf.load_meta_ontology(g)\n",
-        "\n",
-        "adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, contextualize_graph=True, overwrite_graph=True)\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, contextualize_graph=True, overwrite_graph=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9gBg-hDs77i7"
-      },
-      "source": [
-        "# ArangoDB to RDF"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_kkM4P0fWR4e"
-      },
-      "source": [
-        "The `arangodb_graph_to_rdf` and `arangodb_collections_to_rdf` methods return two objects:\n",
-        "\n",
-        "1.   The RDF representation of the ArangoDB Graph, i.e `rdf_graph`\n",
-        "2.   Another RDF Graph mapping the RDF Resources to their designated ArangoDB Collection, i.e `adb_mapping`.\n",
-        "\n",
-        "The second graph, `adb_mapping`, can be re-used in the RDF to ArangoDB (PGT) process to maintain the Document-to-Collection mappings."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UCQ9ppnUQa7e"
-      },
-      "source": [
-        "#### Non-native"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mYZIEAzhQ5CO"
-      },
-      "source": [
-        "Non-native: An ArangoDB Graph that originates from an RDF Context, which has been brought over via one of the `rdf_to_arangodb` methods (RPT/PGT)."
-      ]
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
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-          "output_type": "display_data",
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+      ],
+      "source": [
+        "# Case 3: Data types and language tags\n",
+        "# Case 3.1: Datatype property statements with different data types of the literal objects\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "ex:book ex:publish_date \"1963-03-22\" .\n",
+        "ex:book ex:pages \"100\"^^xsd:integer .\n",
+        "ex:book ex:cover 20 .\n",
+        "ex:book ex:index \"55\" .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
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-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
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+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
-              "
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+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
             ]
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "38e4ec950dfb48c1b86dfa51b59bd9f6",
               "version_major": 2,
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-              "model_id": "aeb95feadd7c4a2ea7ab68e4cef4769e"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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-              "model_id": "68106dfc027d4fe1b68156727f3bae0b"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
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             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b8b1c716475e4043af7919e06253f606",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e77bdee916364cf89751153cb4db42d2"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9c73cea9af174a978ed87abd757dd466",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "999e52c9cf694d38a0e1a97d7a39f123"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "075b504f72064efaaaa1ee09afdc6a9f",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "19cf8acb80354d00bd7eee5247c2b140"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a96caf05dfcf41cb848229c08969a36b",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
             ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 3: Data types and language tags\n",
+        "# Case 3.2: Datatype property statements with different language tags of the literal objects\n",
+        "# NOTE: PGT will currently discard the language tags!\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "\n",
+        "ex:book ex:title \"Book\"@en.\n",
+        "ex:book ex:title \"Bog\"@da.\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 134,
+          "referenced_widgets": [
+            "18a76eb8f00b4c9d93eb862457c93604",
+            "be38c1c4704e42eaab72fa917a31551f",
+            "5819a9a679784e53979fe59ac6fdc6c8",
+            "ea91f40791f247118cbfd30f0a8e0209",
+            "1368dab9ca5d48baabedaa27e63a6608",
+            "cf1f8b24c7b64859b87bfea6a16f7dd6",
+            "1118d6ebf1f74180b2a3fb95d17d5f57",
+            "34f212a9207b4395a7ab6cee9990ba5a",
+            "b4b3ea80874b4f6da320f1c11cf08363",
+            "5a74235176fb4af3831eb25cd2e97262",
+            "99edc92ed36a4566890b4da4ed68c123",
+            "0f786717b0094fe785ea05fec93bdf06",
+            "03b0d5a081d84381929c663657fa266e",
+            "694a30f6080d488cbc087bdb2d91e91e",
+            "aaf936790e47461386f187f15e2fb58c",
+            "c9f80d60eb294a449579d9673201b333",
+            "e9dcbbb06e584549ba0adbf846ad8999",
+            "8ae0c9e3aa7841308a73424fa9fac059",
+            "c050e033d5614e97a63b09513cb6eb10",
+            "9575b3b304784e31a335e371c2d5f3a5",
+            "a43f6762475244aabdff63426a9e1423",
+            "5772089896234e52aa40e3daadc38d30",
+            "1611c20c4e834c76ae76a4f119db71c8",
+            "3b81b38993c14de68df2ff40f0a4f590"
+          ]
+        },
+        "id": "SQaaqperccbA",
+        "outputId": "f35afe70-71d0-498c-a08f-b2ba28b5e9a5"
+      },
+      "outputs": [
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "18a76eb8f00b4c9d93eb862457c93604",
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-              "model_id": "4ed1c45eca21411ab21ac14d326cea64"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
\n",
+              "
\n"
             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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-              "version_minor": 0,
-              "model_id": "ece557abbd214ce2a29e8aed9eed02bb"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
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+            ],
+            "text/plain": []
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+          "output_type": "display_data"
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-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
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+              "
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             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
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+        },
+        {
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-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
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             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
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             "text/plain": [
               "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "display_data",
           "data": {
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               "
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-            ]
+            ],
+            "text/plain": []
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
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+              "
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             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
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             "text/html": [
               "
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-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
+            "text/html": [
+              "
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+              "
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             ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
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-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
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+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
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               "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aaf936790e47461386f187f15e2fb58c",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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             ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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-              "model_id": "262cd26af17341e788a08be05ef71580"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
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+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c050e033d5614e97a63b09513cb6eb10",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
             ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a43f6762475244aabdff63426a9e1423",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ab70def2b20c4aa98ac4b314c1b22725"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "28 21\n",
-            "28 21\n",
-            "--------------------\n",
-            "@prefix ns1:  .\n",
-            "@prefix xsd:  .\n",
-            "\n",
-            "ns1:Imagine a ns1:Album ;\n",
-            "    ns1:artist ns1:John_Lennon ;\n",
-            "    ns1:date \"1971-10-11\" ;\n",
-            "    ns1:name \"Imagine\" .\n",
-            "\n",
-            "ns1:McCartney a ns1:Album ;\n",
-            "    ns1:artist ns1:Paul_McCartney ;\n",
-            "    ns1:date \"1970-04-17\" ;\n",
-            "    ns1:name \"McCartney\" .\n",
-            "\n",
-            "ns1:Please_Please_Me a ns1:Album ;\n",
-            "    ns1:artist ns1:The_Beatles ;\n",
-            "    ns1:date \"1963-03-22\" ;\n",
-            "    ns1:name \"Please Please Me\" ;\n",
-            "    ns1:track ns1:Love_Me_Do .\n",
-            "\n",
-            "ns1:George_Harrison a ns1:SoloArtist .\n",
-            "\n",
-            "ns1:Love_Me_Do a ns1:Song ;\n",
-            "    ns1:length 125 ;\n",
-            "    ns1:name \"Love Me Do\" ;\n",
-            "    ns1:writer ns1:John_Lennon,\n",
-            "        ns1:Paul_McCartney .\n",
-            "\n",
-            "ns1:Ringo_Starr a ns1:SoloArtist .\n",
-            "\n",
-            "ns1:The_Beatles a ns1:Band ;\n",
-            "    ns1:member ns1:George_Harrison,\n",
-            "        ns1:John_Lennon,\n",
-            "        ns1:Paul_McCartney,\n",
-            "        ns1:Ringo_Starr ;\n",
-            "    ns1:name \"The Beatles\" .\n",
-            "\n",
-            "ns1:John_Lennon a ns1:SoloArtist .\n",
-            "\n",
-            "ns1:Paul_McCartney a ns1:SoloArtist .\n",
-            "\n",
-            "\n",
-            "--------------------\n",
-            "@prefix adb:  .\n",
-            "@prefix rdf:  .\n",
-            "\n",
-            " adb:collection \"Class\" .\n",
-            "\n",
-            " adb:collection \"Class\" .\n",
-            "\n",
-            " adb:collection \"SoloArtist\" .\n",
-            "\n",
-            " adb:collection \"Album\" .\n",
-            "\n",
-            " adb:collection \"SoloArtist\" .\n",
-            "\n",
-            " adb:collection \"Song\" .\n",
-            "\n",
-            " adb:collection \"Album\" .\n",
-            "\n",
-            " adb:collection \"SoloArtist\" .\n",
-            "\n",
-            " adb:collection \"Album\" .\n",
-            "\n",
-            " adb:collection \"SoloArtist\" .\n",
-            "\n",
-            " adb:collection \"Class\" .\n",
-            "\n",
-            " adb:collection \"Class\" .\n",
-            "\n",
-            " adb:collection \"Band\" .\n",
-            "\n",
-            " adb:collection \"Property\" .\n",
-            "\n",
-            " adb:collection \"Property\" .\n",
-            "\n",
-            " adb:collection \"Property\" .\n",
-            "\n",
-            " adb:collection \"Property\" .\n",
-            "\n",
-            " adb:collection \"Property\" .\n",
-            "\n",
-            " adb:collection \"Property\" .\n",
-            "\n",
-            " adb:collection \"Property\" .\n",
-            "\n",
-            "rdf:type adb:collection \"Property\" .\n",
-            "\n",
-            "\n",
-            "--------------------\n"
-          ]
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+              "Output()"
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+              "
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       "source": [
-        "data = \"\"\"\n",
-        "PREFIX : \n",
-        "PREFIX rdf: \n",
-        "PREFIX rdfs: \n",
-        "PREFIX xsd: \n",
-        "\n",
-        ":The_Beatles      rdf:type  :Band .\n",
-        ":The_Beatles      :name     \"The Beatles\" .\n",
-        ":The_Beatles      :member   :John_Lennon .\n",
-        ":The_Beatles      :member   :Paul_McCartney .\n",
-        ":The_Beatles      :member   :Ringo_Starr .\n",
-        ":The_Beatles      :member   :George_Harrison .\n",
-        ":John_Lennon      rdf:type  :SoloArtist .\n",
-        ":Paul_McCartney   rdf:type  :SoloArtist .\n",
-        ":Ringo_Starr      rdf:type  :SoloArtist .\n",
-        ":George_Harrison  rdf:type  :SoloArtist .\n",
-        ":Please_Please_Me rdf:type  :Album .\n",
-        ":Please_Please_Me :name     \"Please Please Me\" .\n",
-        ":Please_Please_Me :date     \"1963-03-22\"^^xsd:date .\n",
-        ":Please_Please_Me :artist   :The_Beatles .\n",
-        ":Please_Please_Me :track    :Love_Me_Do .\n",
-        ":Love_Me_Do       rdf:type  :Song .\n",
-        ":Love_Me_Do       :name     \"Love Me Do\" .\n",
-        ":Love_Me_Do       :length   125 .\n",
-        ":Love_Me_Do       :writer   :John_Lennon .\n",
-        ":Love_Me_Do       :writer   :Paul_McCartney .\n",
+        "# Case 4: RDF list\n",
         "\n",
-        ":McCartney        rdf:type  :Album .\n",
-        ":McCartney        :name     \"McCartney\" .\n",
-        ":McCartney        :date     \"1970-04-17\"^^xsd:date .\n",
-        ":McCartney        :artist   :Paul_McCartney .\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
         "\n",
-        ":Imagine          rdf:type  :Album .\n",
-        ":Imagine          :name     \"Imagine\" .\n",
-        ":Imagine          :date     \"1971-10-11\"^^xsd:date .\n",
-        ":Imagine          :artist   :John_Lennon .\n",
+        "ex:List1 ex:contents (\"one\" \"two\" \"three\").\n",
         "\"\"\"\n",
         "\n",
-        "g = Graph()\n",
-        "g.parse(data=data)\n",
-        "\n",
-        "# Selecting RPT or PGT for this example does not matter, as the\n",
-        "# end-result is the same.\n",
-        "adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", g, overwrite_graph=True)\n",
-        "# adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", g, overwrite_graph=True)\n",
-        "\n",
-        "# ArangoDB to RDF via Graph Name\n",
-        "g2, adb_mapping_2 = adbrdf.arangodb_graph_to_rdf(\"DataPGT\", Graph())\n",
-        "\n",
-        "# ArangoDB to RDF via Collection Names\n",
-        "g3, adb_mapping_3 = adbrdf.arangodb_collections_to_rdf(\n",
-        "    \"DataPGT\",\n",
-        "    Graph(),\n",
-        "    v_cols={\"Album\", \"Band\", \"Class\", \"Property\", \"SoloArtist\", \"Song\"},\n",
-        "    e_cols={\"artist\", \"member\", \"track\", \"type\", \"writer\"},\n",
-        ")\n",
-        "\n",
-        "print(len(g2), len(adb_mapping_2))\n",
-        "print(len(g3), len(adb_mapping_3))\n",
-        "\n",
-        "print('--------------------')\n",
-        "print(g2.serialize())\n",
-        "print('--------------------')\n",
-        "print(adb_mapping_2.serialize())\n",
-        "print('--------------------')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uxp9AW7kQkM5"
-      },
-      "source": [
-        "#### Native"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xoza5AvUVqWP"
-      },
-      "source": [
-        "Native: ArangoDB Graphs that originate from an ArangoDB context. We'll be using the [ArangoDB Game Of Thrones Dataset](https://github.com/arangodb/example-datasets/tree/master/GameOfThrones).\n",
-        "\n",
-        "Since we are dealing with a \"native\" ArangoDB Graph, we can rely on the `infer_type_from_adb_col` flag to indicate that `rdf:type` statements of the form (adb_doc rdf:type adb_col) should be inferred upon transferring ArangoDB Documents into RDF.\n",
-        "\n",
-        "We can also take advantage of the `include_adb_key_statements` flag to indicate that `adb:key` statements of the form (adb_doc adb:key adb_doc[\"key\"]) should be generated upon transferring ArangoDB Documents into RDF.\n",
-        "\n",
-        "Note that enabling `infer_type_from_adb_col` `include_adb_key_statements` is only recommended if your ArangoDB graph is \"native\" to ArangoDB. That is, the ArangoDB graph does not originate from an RDF context.\n",
-        "\n",
-        "Finally, we set the `list_conversion_mode` flag to `collection` to indicate that JSON Lists within ArangoDB Documents should be converted into RDF Collections (other options include `container`, and `static`)."
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
       ]
     },
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           "data": {
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-          "text": [
-            "@prefix GameOfThrones:  .\n",
-            "@prefix adb:  .\n",
-            "@prefix ns1:  .\n",
-            "@prefix rdf:  .\n",
-            "@prefix xsd:  .\n",
-            "\n",
-            "GameOfThrones:1926083 a rdf:Statement ;\n",
-            "    adb:key \"1926083\" ;\n",
-            "    rdf:object GameOfThrones:NedStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:RobbStark ;\n",
-            "    GameOfThrones:foo \"bar_1\" .\n",
-            "\n",
-            "GameOfThrones:1926084 a rdf:Statement ;\n",
-            "    adb:key \"1926084\" ;\n",
-            "    rdf:object GameOfThrones:NedStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:SansaStark ;\n",
-            "    GameOfThrones:foo \"bar_2\" .\n",
-            "\n",
-            "GameOfThrones:1926085 a rdf:Statement ;\n",
-            "    adb:key \"1926085\" ;\n",
-            "    rdf:object GameOfThrones:NedStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:AryaStark ;\n",
-            "    GameOfThrones:foo \"bar_3\" .\n",
-            "\n",
-            "GameOfThrones:1926086 a rdf:Statement ;\n",
-            "    adb:key \"1926086\" ;\n",
-            "    rdf:object GameOfThrones:NedStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:BranStark ;\n",
-            "    GameOfThrones:foo true .\n",
-            "\n",
-            "GameOfThrones:1926087 a rdf:Statement ;\n",
-            "    adb:key \"1926087\" ;\n",
-            "    rdf:object GameOfThrones:CatelynStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:RobbStark ;\n",
-            "    GameOfThrones:foo false .\n",
-            "\n",
-            "GameOfThrones:1926088 a rdf:Statement ;\n",
-            "    adb:key \"1926088\" ;\n",
-            "    rdf:object GameOfThrones:CatelynStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:SansaStark ;\n",
-            "    GameOfThrones:foo 6 .\n",
-            "\n",
-            "GameOfThrones:1926089 a rdf:Statement ;\n",
-            "    adb:key \"1926089\" ;\n",
-            "    rdf:object GameOfThrones:CatelynStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:AryaStark ;\n",
-            "    GameOfThrones:foo 7 .\n",
-            "\n",
-            "GameOfThrones:1926090 a rdf:Statement ;\n",
-            "    adb:key \"1926090\" ;\n",
-            "    rdf:object GameOfThrones:CatelynStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:BranStark ;\n",
-            "    GameOfThrones:foo 8 .\n",
-            "\n",
-            "GameOfThrones:1926091 a rdf:Statement ;\n",
-            "    adb:key \"1926091\" ;\n",
-            "    rdf:object GameOfThrones:NedStark ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:JonSnow ;\n",
-            "    GameOfThrones:foo 9 .\n",
-            "\n",
-            "GameOfThrones:1926092 a rdf:Statement ;\n",
-            "    adb:key \"1926092\" ;\n",
-            "    rdf:object GameOfThrones:TywinLannister ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:JaimeLannister ;\n",
-            "    GameOfThrones:foo 1e-01 .\n",
-            "\n",
-            "GameOfThrones:1926093 a rdf:Statement ;\n",
-            "    adb:key \"1926093\" ;\n",
-            "    rdf:object GameOfThrones:TywinLannister ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:CerseiLannister ;\n",
-            "    GameOfThrones:foo 1.1e-01 .\n",
-            "\n",
-            "GameOfThrones:1926094 a rdf:Statement ;\n",
-            "    adb:key \"1926094\" ;\n",
-            "    rdf:object GameOfThrones:TywinLannister ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:TyrionLannister ;\n",
-            "    GameOfThrones:foo 1.2e-01 .\n",
-            "\n",
-            "GameOfThrones:1926095 a rdf:Statement ;\n",
-            "    adb:key \"1926095\" ;\n",
-            "    rdf:object GameOfThrones:CerseiLannister ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:JoffreyBaratheon ;\n",
-            "    GameOfThrones:foo ( \"bar_13\" ) .\n",
-            "\n",
-            "GameOfThrones:1926096 a rdf:Statement ;\n",
-            "    adb:key \"1926096\" ;\n",
-            "    rdf:object GameOfThrones:JaimeLannister ;\n",
-            "    rdf:predicate GameOfThrones:ChildOf ;\n",
-            "    rdf:subject GameOfThrones:JoffreyBaratheon ;\n",
-            "    GameOfThrones:foo ( ( \"bar_14\" ) ( \"bar_15\" ( \"bar_16\" ) ) ) .\n",
-            "\n",
-            "GameOfThrones:1926098 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"Dragonstone\" ;\n",
-            "    adb:key \"1926098\" ;\n",
-            "    GameOfThrones:coordinate ( 5.51678e+01 -6.815096e+00 ) .\n",
-            "\n",
-            "GameOfThrones:1926099 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"King's Landing\" ;\n",
-            "    adb:key \"1926099\" ;\n",
-            "    GameOfThrones:coordinate ( 4.263975e+01 1.811019e+01 ) .\n",
-            "\n",
-            "GameOfThrones:1926100 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"The Red Keep\" ;\n",
-            "    adb:key \"1926100\" ;\n",
-            "    GameOfThrones:coordinate ( 3.589645e+01 1.444644e+01 ) .\n",
-            "\n",
-            "GameOfThrones:1926101 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"Yunkai\" ;\n",
-            "    adb:key \"1926101\" ;\n",
-            "    GameOfThrones:coordinate ( 3.104664e+01 -7.129532e+00 ) .\n",
-            "\n",
-            "GameOfThrones:1926102 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"Astapor\" ;\n",
-            "    adb:key \"1926102\" ;\n",
-            "    GameOfThrones:coordinate ( 3.150974e+01 -9.774249e+00 ) .\n",
-            "\n",
-            "GameOfThrones:1926103 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"Winterfell\" ;\n",
-            "    adb:key \"1926103\" ;\n",
-            "    GameOfThrones:coordinate ( 5.436832e+01 -5.581312e+00 ) .\n",
-            "\n",
-            "GameOfThrones:1926104 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"Vaes Dothrak\" ;\n",
-            "    adb:key \"1926104\" ;\n",
-            "    GameOfThrones:coordinate ( 5.416776e+01 -6.096125e+00 ) .\n",
-            "\n",
-            "GameOfThrones:1926105 a GameOfThrones:Locations ;\n",
-            "    ns1:name \"Beyond the wall\" ;\n",
-            "    adb:key \"1926105\" ;\n",
-            "    GameOfThrones:coordinate ( 6.426547e+01 -2.109409e+01 ) .\n",
-            "\n",
-            "GameOfThrones:A a GameOfThrones:Traits ;\n",
-            "    adb:key \"A\" ;\n",
-            "    GameOfThrones:de \"stark\" ;\n",
-            "    GameOfThrones:en \"strong\" .\n",
-            "\n",
-            "GameOfThrones:B a GameOfThrones:Traits ;\n",
-            "    adb:key \"B\" ;\n",
-            "    GameOfThrones:de \"freundlich\" ;\n",
-            "    GameOfThrones:en \"polite\" .\n",
-            "\n",
-            "GameOfThrones:BrienneTarth a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Brienne\" ;\n",
-            "    adb:key \"BrienneTarth\" ;\n",
-            "    GameOfThrones:age 32 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Tarth\" ;\n",
-            "    GameOfThrones:traits ( \"P\" \"C\" \"A\" \"K\" ) .\n",
-            "\n",
-            "GameOfThrones:Bronn a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Bronn\" ;\n",
-            "    adb:key \"Bronn\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:traits ( \"K\" \"E\" \"C\" ) .\n",
-            "\n",
-            "GameOfThrones:C a GameOfThrones:Traits ;\n",
-            "    adb:key \"C\" ;\n",
-            "    GameOfThrones:de \"loyal\" ;\n",
-            "    GameOfThrones:en \"loyal\" .\n",
-            "\n",
-            "GameOfThrones:D a GameOfThrones:Traits ;\n",
-            "    adb:key \"D\" ;\n",
-            "    GameOfThrones:de \"schön\" ;\n",
-            "    GameOfThrones:en \"beautiful\" .\n",
-            "\n",
-            "GameOfThrones:DaarioNaharis a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Daario\" ;\n",
-            "    adb:key \"DaarioNaharis\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Naharis\" ;\n",
-            "    GameOfThrones:traits ( \"K\" \"P\" \"A\" ) .\n",
-            "\n",
-            "GameOfThrones:DaenerysTargaryen a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Daenerys\" ;\n",
-            "    adb:key \"DaenerysTargaryen\" ;\n",
-            "    GameOfThrones:age 16 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Targaryen\" ;\n",
-            "    GameOfThrones:traits ( \"D\" \"H\" \"C\" ) .\n",
-            "\n",
-            "GameOfThrones:DavosSeaworth a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Davos\" ;\n",
-            "    adb:key \"DavosSeaworth\" ;\n",
-            "    GameOfThrones:age 49 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Seaworth\" ;\n",
-            "    GameOfThrones:traits ( \"C\" \"K\" \"P\" \"F\" ) .\n",
-            "\n",
-            "GameOfThrones:E a GameOfThrones:Traits ;\n",
-            "    adb:key \"E\" ;\n",
-            "    GameOfThrones:de \"hinterlistig\" ;\n",
-            "    GameOfThrones:en \"sneaky\" .\n",
-            "\n",
-            "GameOfThrones:EllariaSand a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Ellaria\" ;\n",
-            "    adb:key \"EllariaSand\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Sand\" ;\n",
-            "    GameOfThrones:traits ( \"P\" \"O\" \"A\" \"E\" ) .\n",
-            "\n",
-            "GameOfThrones:F a GameOfThrones:Traits ;\n",
-            "    adb:key \"F\" ;\n",
-            "    GameOfThrones:de \"erfahren\" ;\n",
-            "    GameOfThrones:en \"experienced\" .\n",
-            "\n",
-            "GameOfThrones:G a GameOfThrones:Traits ;\n",
-            "    adb:key \"G\" ;\n",
-            "    GameOfThrones:de \"korrupt\" ;\n",
-            "    GameOfThrones:en \"corrupt\" .\n",
-            "\n",
-            "GameOfThrones:Gendry a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Gendry\" ;\n",
-            "    adb:key \"Gendry\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:traits ( \"K\" \"C\" \"A\" ) .\n",
-            "\n",
-            "GameOfThrones:Gilly a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Gilly\" ;\n",
-            "    adb:key \"Gilly\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:traits ( \"L\" \"J\" ) .\n",
-            "\n",
-            "GameOfThrones:H a GameOfThrones:Traits ;\n",
-            "    adb:key \"H\" ;\n",
-            "    GameOfThrones:de \"einflussreich\" ;\n",
-            "    GameOfThrones:en \"powerful\" .\n",
-            "\n",
-            "GameOfThrones:I a GameOfThrones:Traits ;\n",
-            "    adb:key \"I\" ;\n",
-            "    GameOfThrones:de \"naiv\" ;\n",
-            "    GameOfThrones:en \"naive\" .\n",
-            "\n",
-            "GameOfThrones:J a GameOfThrones:Traits ;\n",
-            "    adb:key \"J\" ;\n",
-            "    GameOfThrones:de \"unverheiratet\" ;\n",
-            "    GameOfThrones:en \"unmarried\" .\n",
-            "\n",
-            "GameOfThrones:JaqenHghar a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Jaqen\" ;\n",
-            "    adb:key \"JaqenHghar\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"H'ghar\" ;\n",
-            "    GameOfThrones:traits ( \"H\" \"F\" \"K\" ) .\n",
-            "\n",
-            "GameOfThrones:JeorMormont a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Jeor\" ;\n",
-            "    adb:key \"JeorMormont\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Mormont\" ;\n",
-            "    GameOfThrones:traits ( \"C\" \"H\" \"M\" \"P\" ) .\n",
-            "\n",
-            "GameOfThrones:JorahMormont a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Jorah\" ;\n",
-            "    adb:key \"JorahMormont\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Mormont\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"B\" \"C\" \"F\" ) .\n",
-            "\n",
-            "GameOfThrones:K a GameOfThrones:Traits ;\n",
-            "    adb:key \"K\" ;\n",
-            "    GameOfThrones:de \"geschickt\" ;\n",
-            "    GameOfThrones:en \"skillful\" .\n",
-            "\n",
-            "GameOfThrones:KhalDrogo a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Khal\" ;\n",
-            "    adb:key \"KhalDrogo\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Drogo\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"C\" \"O\" \"P\" ) .\n",
-            "\n",
-            "GameOfThrones:L a GameOfThrones:Traits ;\n",
-            "    adb:key \"L\" ;\n",
-            "    GameOfThrones:de \"jung\" ;\n",
-            "    GameOfThrones:en \"young\" .\n",
-            "\n",
-            "GameOfThrones:M a GameOfThrones:Traits ;\n",
-            "    adb:key \"M\" ;\n",
-            "    GameOfThrones:de \"klug\" ;\n",
-            "    GameOfThrones:en \"smart\" .\n",
-            "\n",
-            "GameOfThrones:MargaeryTyrell a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Margaery\" ;\n",
-            "    adb:key \"MargaeryTyrell\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Tyrell\" ;\n",
-            "    GameOfThrones:traits ( \"M\" \"D\" \"B\" ) .\n",
-            "\n",
-            "GameOfThrones:Melisandre a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Melisandre\" ;\n",
-            "    adb:key \"Melisandre\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:traits ( \"G\" \"E\" \"H\" ) .\n",
-            "\n",
-            "GameOfThrones:Missandei a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Missandei\" ;\n",
-            "    adb:key \"Missandei\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:traits ( \"D\" \"L\" \"C\" \"M\" ) .\n",
-            "\n",
-            "GameOfThrones:N a GameOfThrones:Traits ;\n",
-            "    adb:key \"N\" ;\n",
-            "    GameOfThrones:de \"rational\" ;\n",
-            "    GameOfThrones:en \"rational\" .\n",
-            "\n",
-            "GameOfThrones:O a GameOfThrones:Traits ;\n",
-            "    adb:key \"O\" ;\n",
-            "    GameOfThrones:de \"skrupellos\" ;\n",
-            "    GameOfThrones:en \"ruthless\" .\n",
-            "\n",
-            "GameOfThrones:P a GameOfThrones:Traits ;\n",
-            "    adb:key \"P\" ;\n",
-            "    GameOfThrones:de \"mutig\" ;\n",
-            "    GameOfThrones:en \"brave\" .\n",
-            "\n",
-            "GameOfThrones:PetyrBaelish a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Petyr\" ;\n",
-            "    adb:key \"PetyrBaelish\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Baelish\" ;\n",
-            "    GameOfThrones:traits ( \"E\" \"G\" \"F\" ) .\n",
-            "\n",
-            "GameOfThrones:Q a GameOfThrones:Traits ;\n",
-            "    adb:key \"Q\" ;\n",
-            "    GameOfThrones:de \"mächtig\" ;\n",
-            "    GameOfThrones:en \"mighty\" .\n",
-            "\n",
-            "GameOfThrones:R a GameOfThrones:Traits ;\n",
-            "    adb:key \"R\" ;\n",
-            "    GameOfThrones:de \"schwach\" ;\n",
-            "    GameOfThrones:en \"weak\" .\n",
-            "\n",
-            "GameOfThrones:RamsayBolton a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Ramsay\" ;\n",
-            "    adb:key \"RamsayBolton\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Bolton\" ;\n",
-            "    GameOfThrones:traits ( \"E\" \"O\" \"G\" \"A\" ) .\n",
-            "\n",
-            "GameOfThrones:RobertBaratheon a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Robert\" ;\n",
-            "    adb:key \"RobertBaratheon\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Baratheon\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"H\" \"C\" ) .\n",
-            "\n",
-            "GameOfThrones:RooseBolton a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Roose\" ;\n",
-            "    adb:key \"RooseBolton\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Bolton\" ;\n",
-            "    GameOfThrones:traits ( \"H\" \"E\" \"F\" \"A\" ) .\n",
-            "\n",
-            "GameOfThrones:SamwellTarly a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Samwell\" ;\n",
-            "    adb:key \"SamwellTarly\" ;\n",
-            "    GameOfThrones:age 17 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Tarly\" ;\n",
-            "    GameOfThrones:traits ( \"C\" \"L\" \"I\" ) .\n",
-            "\n",
-            "GameOfThrones:SandorClegane a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Sandor\" ;\n",
-            "    adb:key \"SandorClegane\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Clegane\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"P\" \"K\" \"F\" ) .\n",
-            "\n",
-            "GameOfThrones:Shae a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Shae\" ;\n",
-            "    adb:key \"Shae\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:traits ( \"M\" \"D\" \"G\" ) .\n",
-            "\n",
-            "GameOfThrones:StannisBaratheon a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Stannis\" ;\n",
-            "    adb:key \"StannisBaratheon\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Baratheon\" ;\n",
-            "    GameOfThrones:traits ( \"H\" \"O\" \"P\" \"M\" ) .\n",
-            "\n",
-            "GameOfThrones:TalisaMaegyr a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Talisa\" ;\n",
-            "    adb:key \"TalisaMaegyr\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Maegyr\" ;\n",
-            "    GameOfThrones:traits ( \"D\" \"C\" \"B\" ) .\n",
-            "\n",
-            "GameOfThrones:TheHighSparrow a GameOfThrones:Characters ;\n",
-            "    ns1:name \"The High Sparrow\" ;\n",
-            "    adb:key \"TheHighSparrow\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:traits ( \"H\" \"M\" \"F\" \"O\" ) .\n",
-            "\n",
-            "GameOfThrones:TheonGreyjoy a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Theon\" ;\n",
-            "    adb:key \"TheonGreyjoy\" ;\n",
-            "    GameOfThrones:age 16 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Greyjoy\" ;\n",
-            "    GameOfThrones:traits ( \"E\" \"R\" \"K\" ) .\n",
-            "\n",
-            "GameOfThrones:TommenBaratheon a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Tommen\" ;\n",
-            "    adb:key \"TommenBaratheon\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Baratheon\" ;\n",
-            "    GameOfThrones:traits ( \"I\" \"L\" \"B\" ) .\n",
-            "\n",
-            "GameOfThrones:TormundGiantsbane a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Tormund\" ;\n",
-            "    adb:key \"TormundGiantsbane\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Giantsbane\" ;\n",
-            "    GameOfThrones:traits ( \"C\" \"P\" \"A\" \"I\" ) .\n",
-            "\n",
-            "GameOfThrones:Varys a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Varys\" ;\n",
-            "    adb:key \"Varys\" ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:traits ( \"M\" \"F\" \"N\" \"E\" ) .\n",
-            "\n",
-            "GameOfThrones:ViserysTargaryen a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Viserys\" ;\n",
-            "    adb:key \"ViserysTargaryen\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Targaryen\" ;\n",
-            "    GameOfThrones:traits ( \"O\" \"L\" \"N\" ) .\n",
-            "\n",
-            "GameOfThrones:Ygritte a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Ygritte\" ;\n",
-            "    adb:key \"Ygritte\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:traits ( \"A\" \"P\" \"K\" ) .\n",
-            "\n",
-            "GameOfThrones:JonSnow a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Jon\" ;\n",
-            "    adb:key \"JonSnow\" ;\n",
-            "    GameOfThrones:age 16 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Snow\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"B\" \"C\" \"F\" ) .\n",
-            "\n",
-            "GameOfThrones:TyrionLannister a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Tyrion\" ;\n",
-            "    adb:key \"TyrionLannister\" ;\n",
-            "    GameOfThrones:age 32 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Lannister\" ;\n",
-            "    GameOfThrones:traits ( \"F\" \"K\" \"M\" \"N\" ) .\n",
-            "\n",
-            "GameOfThrones:AryaStark a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Arya\" ;\n",
-            "    adb:key \"AryaStark\" ;\n",
-            "    GameOfThrones:age 11 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Stark\" ;\n",
-            "    GameOfThrones:traits ( \"C\" \"K\" \"L\" ) .\n",
-            "\n",
-            "GameOfThrones:BranStark a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Bran\" ;\n",
-            "    adb:key \"BranStark\" ;\n",
-            "    GameOfThrones:age 10 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Stark\" ;\n",
-            "    GameOfThrones:traits ( \"L\" \"J\" ) .\n",
-            "\n",
-            "GameOfThrones:CerseiLannister a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Cersei\" ;\n",
-            "    adb:key \"CerseiLannister\" ;\n",
-            "    GameOfThrones:age 36 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Lannister\" ;\n",
-            "    GameOfThrones:traits ( \"H\" \"E\" \"F\" ) .\n",
-            "\n",
-            "GameOfThrones:JaimeLannister a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Jaime\" ;\n",
-            "    adb:key \"JaimeLannister\" ;\n",
-            "    GameOfThrones:age 36 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Lannister\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"F\" \"B\" ) .\n",
-            "\n",
-            "GameOfThrones:JoffreyBaratheon a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Joffrey\" ;\n",
-            "    adb:key \"JoffreyBaratheon\" ;\n",
-            "    GameOfThrones:age 19 ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Baratheon\" ;\n",
-            "    GameOfThrones:traits ( \"I\" \"L\" \"O\" ) .\n",
-            "\n",
-            "GameOfThrones:RobbStark a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Robb\" ;\n",
-            "    adb:key \"RobbStark\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Stark\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"B\" \"C\" \"K\" ) .\n",
-            "\n",
-            "GameOfThrones:SansaStark a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Sansa\" ;\n",
-            "    adb:key \"SansaStark\" ;\n",
-            "    GameOfThrones:age 13 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Stark\" ;\n",
-            "    GameOfThrones:traits ( \"D\" \"I\" \"J\" ) .\n",
-            "\n",
-            "GameOfThrones:TywinLannister a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Tywin\" ;\n",
-            "    adb:key \"TywinLannister\" ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Lannister\" ;\n",
-            "    GameOfThrones:traits ( \"O\" \"M\" \"H\" \"F\" ) .\n",
-            "\n",
-            "GameOfThrones:CatelynStark a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Catelyn\" ;\n",
-            "    adb:key \"CatelynStark\" ;\n",
-            "    GameOfThrones:age 40 ;\n",
-            "    GameOfThrones:alive false ;\n",
-            "    GameOfThrones:surname \"Stark\" ;\n",
-            "    GameOfThrones:traits ( \"D\" \"H\" \"C\" ) .\n",
-            "\n",
-            "GameOfThrones:NedStark a GameOfThrones:Characters ;\n",
-            "    ns1:name \"Ned\" ;\n",
-            "    adb:key \"NedStark\" ;\n",
-            "    GameOfThrones:age 41 ;\n",
-            "    GameOfThrones:alive true ;\n",
-            "    GameOfThrones:surname \"Stark\" ;\n",
-            "    GameOfThrones:traits ( \"A\" \"H\" \"C\" \"N\" \"P\" ) .\n",
-            "\n",
-            "\n"
-          ]
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "750e03bd0e7340ea94112bb5971b556d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "85eb298ec6d649c4bef732d7776e1936",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "341b818f59ba43d4b69166e655dd9765",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "283c95e105104ea6a5b235ede8a1a2ab",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
-        "rdf_graph, adb_mapping = adbrdf.arangodb_graph_to_rdf(\"GameOfThrones\", rdf_graph=Graph(), list_conversion_mode=\"collection\", infer_type_from_adb_v_col=True, include_adb_key_statements=True)\n",
-        "print(rdf_graph.serialize())"
+        "# Case 5: Blank nodes\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "\n",
+        "ex:bob ex:nationality _:c .\n",
+        "_:c ex:country \"Canada\" .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
       ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [
-        "44mc2EvIAzDy",
-        "yRuJ3OIGE2Yr",
-        "KnQifktFAxHx",
-        "7y81WHO8eG8_",
-        "QfE_tKxneG9A",
-        "znQCjOwt7zBz",
-        "0qry3Bcy-160",
-        "mRutdKii-Pk5",
-        "0SWi4e3wIMtw",
-        "9gBg-hDs77i7",
-        "UCQ9ppnUQa7e",
-        "uxp9AW7kQkM5"
-      ],
-      "provenance": []
-    },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
     },
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {
-        "b7fc8abdf6814a58bd2fa09ae4421720": {
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 150,
+          "referenced_widgets": [
+            "1fc43d8d945449ff8bf650a7168f3175",
+            "c06094c52f354bf887b9573ef728ba6f",
+            "597dc766da6d4c65a6e526f29d8001ad",
+            "6c9fda1cf8f441649b19421140844eca",
+            "222ddec771e2487088f38f5d8abc93d6",
+            "ca9ee619db5145a7b14ef9eaf7b282ef",
+            "90c5c0ba3d0b4e8e8194dc0bc254b191",
+            "d773e247a00b40f592d2b8a355d67eae",
+            "5f82c603b82e424daed6d81cc12810b3",
+            "94d0232fdf1a46b8ad11059dbf8162dc",
+            "cb0587dfec79428a8171b6e29aa7b75c",
+            "a8a41f5965ec45ed90c876c0b309b044",
+            "fca5445e52624e6aabf3acea3b614d1a",
+            "d793b865039d47fa966eb68c790b62a6",
+            "afd95022a638474ebd254a4e365e0f5f",
+            "787d5500e7da4133a75f8df78b8466fa",
+            "b98c2692fc3c4837a405d58e2cb2aede",
+            "a05e21f6174647dca22307a6ae256d40",
+            "549cc0eeb21e4816af1ce48c6992db04",
+            "863bb5095d89492cb6153b3bbde5b104",
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+            "ddf39a4764c84bafb454073773b0b5f3",
+            "48867504843843389281e9c6c12953c4",
+            "84276bda9c6c4c7280423540f4695001"
+          ]
+        },
+        "id": "noAXcHOJFJvG",
+        "outputId": "554e4310-e28b-40a7-a701-a2ac91affdaf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1fc43d8d945449ff8bf650a7168f3175",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
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+              "model_id": "222ddec771e2487088f38f5d8abc93d6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
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+              "model_id": "90c5c0ba3d0b4e8e8194dc0bc254b191",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cb0587dfec79428a8171b6e29aa7b75c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "fca5445e52624e6aabf3acea3b614d1a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "afd95022a638474ebd254a4e365e0f5f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b98c2692fc3c4837a405d58e2cb2aede",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "549cc0eeb21e4816af1ce48c6992db04",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ba683955f5904919ad09b805dbbf6a5c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "48867504843843389281e9c6c12953c4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 6: Named graphs\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix rdfs:  .\n",
+        "\n",
+        "ex:Monica ex:employer ex:ArangoDB .\n",
+        "\n",
+        "ex:Graph1 {\n",
+        "    ex:Monica a ex:Entity .\n",
+        "    ex:Management a ex:Skill .\n",
+        "    ex:Monica ex:name \"Monica\" .\n",
+        "    ex:Monica ex:homepage  .\n",
+        "    ex:Monica ex:hasSkill ex:Management .\n",
+        "    ex:Monica ex:dateOfBirth \"1963-03-22\".\n",
+        "}\n",
+        "\n",
+        "ex:Graph2 {\n",
+        "    ex:Programming a ex:Skill .\n",
+        "     a ex:Website .\n",
+        "    ex:Monica a ex:Person .\n",
+        "    ex:Person rdfs:subClassOf ex:Entity .\n",
+        "    ex:Monica ex:hasSkill ex:Programming .\n",
+        "}\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 134,
+          "referenced_widgets": [
+            "7f4735b6532b44bf9e2a4deb1385e677",
+            "e86234b816a148f482c6f0d7fc241675",
+            "fbb89f7404f644b4adcb2e011bd39a2f",
+            "d2a5ec6f84244ed58b1cbf3fadcffe9d",
+            "f3091863c2ce495b8846ca16c240bac6",
+            "3c6e454224c346ae820de03d069583ad",
+            "1d6a2e9d98e6431db87f0f20de68a2db",
+            "ef946e47d6e74ed6a4441e6aeba6ac07",
+            "3559d4b574044e0e8bace41211b1649f",
+            "2b8042363eca490d9be41f7876e0aab0",
+            "a7ba9072d835401592f843ed6fe9a334",
+            "47bee386693546e4906a547ff5556cb2",
+            "14c1ae08e37343c683e4a8a3f8173d79",
+            "f5b15a86202840cca452980125b90c48",
+            "de3bbb53c6704b888d9b9754434bfdb9",
+            "ee7a0a507eae44e192b9d10ffdaf26de",
+            "a2798e676ae644a0bc091c23c1e7c5c1",
+            "ffc83e4385534117bc664c291338f355",
+            "5fdae6f5461e462dad3e5309fffe4cc3",
+            "db4f225255194d17bb18112c8a4d10e6",
+            "ea833033e1c24a8faf3bec5e29418b5e",
+            "b9c6b47990fe48aab0970677ab004b46",
+            "ea9cbfeffdc64559af9b768846b634a5",
+            "326423bae4c54057ad221e7c3077ad64",
+            "05cf4bc8861048029fcf6df306e7c8d0",
+            "c62a384e50324db581016107e3a22952"
+          ]
+        },
+        "id": "gtHKG7PiGyyF",
+        "outputId": "5bd57d09-d663-4156-9df3-3725eeb33aee"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7f4735b6532b44bf9e2a4deb1385e677",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "fbb89f7404f644b4adcb2e011bd39a2f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
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+              "Output()"
+            ]
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+          "output_type": "display_data"
+        },
+        {
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+              "
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+            ],
+            "text/plain": []
+          },
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+        }
+      ],
+      "source": [
+        "# Case 7: Multiple types for resources - rdf:type\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix :  .\n",
+        "@prefix rdfs:  .\n",
+        "@prefix adb:  .\n",
+        "@prefix owl:  .\n",
+        "\n",
+        ":alice a :Arson .\n",
+        ":alice a :Author .\n",
+        "\n",
+        ":Zenkey rdfs:subClassOf :Zebra .\n",
+        ":Zenkey rdfs:subClassOf :Donkey .\n",
+        ":Donkey rdfs:subClassOf :Animal .\n",
+        ":Zebra rdfs:subClassOf :Animal .\n",
+        ":Human rdfs:subClassOf :Animal .\n",
+        ":Animal rdfs:subClassOf :LivingThing .\n",
+        ":LivingThing rdfs:subClassOf :Thing .\n",
+        ":Thing rdfs:subClassOf :Object .\n",
+        "\n",
+        ":charlie a :LivingThing .\n",
+        ":charlie a :Animal .\n",
+        ":charlie a :Zenkey .\n",
+        "\n",
+        ":marty a :LivingThing .\n",
+        ":marty a :Animal .\n",
+        ":marty a :Human .\n",
+        ":marty a :Author .\n",
+        "\n",
+        ":john a :Singer .\n",
+        ":john a :Writer .\n",
+        ":john a :Guitarist .\n",
+        ":john adb:collection \"Artist\" .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7ZeZNno5k3XW"
+      },
+      "source": [
+        "Cases 8 - 15: RDF-Star\n",
+        "\n",
+        "The [rdflib](https://github.com/RDFLib/rdflib) package hasn't introduced support for [Quoted Triples](https://www.w3.org/TR/rdf12-concepts/#dfn-quoted-triple) yet, so ArangoRDF's support for RDF-star is based on [Triple Reification](https://www.w3.org/wiki/RdfReification)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
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+              "Output()"
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+              "
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+              "\n"
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+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
+          "metadata": {},
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+        },
+        {
+          "data": {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cbf2ac2f71e24256bcae1e4321e39d58",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 8:  Embedded object property statement in subject position\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:alice;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:bob ;\n",
+        "    ex:certainty \"0.5\"^^xsd:double .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84,
+          "referenced_widgets": [
+            "b69a1ecf8f1447ddb4f04e55212f6d97",
+            "5f98553c1f4f4594a4b04d519700c077",
+            "b669c7e6ace146ba8a5b4a2acd29064f",
+            "da95283e26034646ad205320aa8900ad",
+            "eb609f79887b4c7b9c796b802290f645",
+            "3983a55e940b4729869ddcc63b83bb51",
+            "abdad6f869ba40529d63e6ca8d4b854c",
+            "09c18b9e1bfa46e297e2476df6de6349"
+          ]
+        },
+        "id": "KAs-MpmAp8_c",
+        "outputId": "ef925d15-c87d-44af-fc8d-f879adb94592"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b69a1ecf8f1447ddb4f04e55212f6d97",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b669c7e6ace146ba8a5b4a2acd29064f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "eb609f79887b4c7b9c796b802290f645",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "abdad6f869ba40529d63e6ca8d4b854c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 9: Embedded datatype property statement in subject position\n",
+        "# Note: PGT does not support this case yet\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:mark;\n",
+        "    rdf:predicate ex:age;\n",
+        "    rdf:object 28 ;\n",
+        "    ex:certainty 1 .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 184,
+          "referenced_widgets": [
+            "f500a92dca614fbe97190ea07b76f391",
+            "1980f083a8a24d118d4929daae64b05d",
+            "b4da93b0d8664c74bfffcd2fdf727d01",
+            "8182af5eea8247f38b41d468c10b2ce5",
+            "68ed047e78784348b8478e06f36498da",
+            "841e8ab2c5f044b4a1fadd8c2acd4a89",
+            "a32bca072f5c491cae9947d2b4cd5de9",
+            "6583d4fa4e8e465dbd8a6f7e903f9766",
+            "0e59fe845b1f474181c50e61c986780c",
+            "ba49567d52134f59be958023a178943e",
+            "5feded6877d94084a1426ceec3d0baca",
+            "31f235ab96ac4c3e866a1f42525bbf08",
+            "2da044ff861542359703ca9ae6c0d56b",
+            "a7a62d0a2adb4945bbff664966216d33",
+            "e91a8d9c045142c88600528a20845161",
+            "8de688d21cf147988f21e29a740ff08c",
+            "1b529365878647f980449bb9b38b9460",
+            "e34293eb4991417b9519a1029276803a",
+            "5a62d99ec49d4d2181f4296e97334516",
+            "4499a8f97cc4412db02dbe138ccf9357",
+            "2f40386b3fd1461b91d3809794504b5c",
+            "2d502c4cd2ce41cfa6e35eff82ebe098",
+            "51c97d40286f4bd9b922aedf3fd912a2",
+            "9134cccccc79418d887f13938c68d35e"
+          ]
+        },
+        "id": "_ZWrGS9Uqoc1",
+        "outputId": "c150db3c-418d-45f7-ba26-0b5fb44a5eb6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f500a92dca614fbe97190ea07b76f391",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b4da93b0d8664c74bfffcd2fdf727d01",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "68ed047e78784348b8478e06f36498da",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "model_id": "a32bca072f5c491cae9947d2b4cd5de9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e91a8d9c045142c88600528a20845161",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1b529365878647f980449bb9b38b9460",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5a62d99ec49d4d2181f4296e97334516",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2f40386b3fd1461b91d3809794504b5c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "51c97d40286f4bd9b922aedf3fd912a2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 10: Embedded object property statement in object position\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "_:x a rdf:Statement;\n",
+        "    rdf:subject ex:mainPage;\n",
+        "    rdf:predicate ex:writer;\n",
+        "    rdf:object ex:alice;\n",
+        "    ex:1 \"1\";\n",
+        "    ex:2 \"2\";\n",
+        "    ex:3 \"3\" .\n",
+        "\n",
+        "ex:bobshomepage ex:source _:x .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 167,
+          "referenced_widgets": [
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+            "ea6b9fdbcaa14135be29b6980706130f",
+            "f3738174a0b445429dc6942b109d7b23",
+            "3ec735ec0be8406e93b98c8fc5faa94d"
+          ]
+        },
+        "id": "E_iK33XDSiml",
+        "outputId": "584aa990-7441-40b5-edc9-a8bb86022143"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0f9c78460dd242fb8aae8d90beb5de1e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
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+        {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_minor": 0
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ebd4df3b9f144ea18b91234936f1b6a2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7a9da186117340f18f557f39131f3ab3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
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+              "model_id": "31c785fe99cb45488b2370510428f2f9",
+              "version_major": 2,
+              "version_minor": 0
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2184f8202fbe4d359a0d611cdedb70c7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f2ea318f46fa4045898a74adb4282073",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ae2da01ed0d642abb114323e9f67d829",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c0e10fa9eeac4a66ab0a90c7b3c2fe80",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f3738174a0b445429dc6942b109d7b23",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 11: Embedded object property statement in subject position and non-literal object\n",
+        "# Case 11.1: Asserted statement with non-literal object\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:mainPage;\n",
+        "    rdf:predicate ex:writer;\n",
+        "    rdf:object ex:alice ;\n",
+        "    ex:source ex:bobshomepage .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 184,
+          "referenced_widgets": [
+            "92282a6db94f41f79fe9e13b9354aadf",
+            "4a397ba17bd747a185a82cf1a1fb4637",
+            "223f64295def4d82b07989a0e9171191",
+            "eff7fd5f3f574ed38cfcafb5495e83c0",
+            "56658e42d18f4db7b09c3dff62b0e7cf",
+            "d7e349f506e34b12b72bc284c874c515",
+            "ce2a57c811794249afb7fa5c49af992b",
+            "b29eb50fc0bc4a7b9abf6a7da3042f72",
+            "f63d3988a9ce4174bd5e85f2f87b29b9",
+            "393a3b2b1fab47b3836ce742c9491594",
+            "10b486782c364980b39db66ee20b7834",
+            "82153626f6e34225908e5d49fa73d5b1",
+            "f98fd73455d24b718e6c0217cacc5ebb",
+            "d8ee71ebbae045fd82b67ed6fcb1d2ca",
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+            "f12398082c0d4dd3961a166418f65f51",
+            "e097249a1e6c484f9d1c54e8e76bf5a4",
+            "aa947d9e955b476686905afb94bd9a30",
+            "ac20f1f535434e80b9db35fdf03f4c60",
+            "3735f9c2a9924655ae67398428eb9c14",
+            "e39bf2d80ac94271bdeabee0c0c3cbb1"
+          ]
+        },
+        "id": "0oZbDeLeS6ll",
+        "outputId": "aae04df7-3caf-4278-f11f-5d0e1c243895"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "92282a6db94f41f79fe9e13b9354aadf",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "223f64295def4d82b07989a0e9171191",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
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+              "
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+            ],
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+              "\n"
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "\n"
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "\n"
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2ff255c0ad7f4b6db1681395395073e6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f12398082c0d4dd3961a166418f65f51",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aa947d9e955b476686905afb94bd9a30",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3735f9c2a9924655ae67398428eb9c14",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 11: Embedded object property statement in subject position and non-literal object\n",
+        "# Case 11.2: Asserted statement with non-literal object that appears in another asserted statement\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "ex:alex ex:age 25 .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:alice;\n",
+        "    rdf:predicate ex:friend;\n",
+        "    rdf:object ex:bob ;\n",
+        "    ex:mentionedBy ex:alex .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 167,
+          "referenced_widgets": [
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+            "d711c89e86da452ba56a5d0fa177fc6e",
+            "bafb6c71b2174ce5a3b261d3c48cf0b4"
+          ]
+        },
+        "id": "woNfHiZ5S__t",
+        "outputId": "b547b01d-724e-4c65-de21-68ccb631ca48"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "510794a7ae37442f8b66a3a74661de66",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
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+        },
+        {
+          "data": {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
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+            "text/plain": [
+              "Output()"
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+        {
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+              "
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+              "
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+              "\n"
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+            "text/plain": [
+              "Output()"
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+              "version_major": 2,
+              "version_minor": 0
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "
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+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "model_id": "1eecac5b4e75476abeaea6ec99823841",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d711c89e86da452ba56a5d0fa177fc6e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n",
+        "# Case 12.1: Asserted statement with rdf:type as predicate\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:mainPage;\n",
+        "    rdf:predicate ex:writer;\n",
+        "    rdf:object ex:alice ;\n",
+        "    rdf:type ex:bobshomepage .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 167,
+          "referenced_widgets": [
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+            "46356aae8ddc45ffa519a783411ed5c3",
+            "b1664273e12e42fe9ffc87283ee1ac17",
+            "3b77a9b7d5a54d0bb587666d26fe9638",
+            "9f871650a7f9452a972e3fb359f6202c",
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+            "2ebcb5a1f513410f8f703da16b3f44c1",
+            "de930ca00f7d4853afa6d2cd832ecd06",
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+            "47de40701c0e44b4980e966e2feeefe2",
+            "07a08a86cc2c4981beb1ff4d1715e4c0",
+            "f711293be6064fbeb78a10e36fb4ca3c"
+          ]
+        },
+        "id": "lbbdb2lwS_2M",
+        "outputId": "0b252df3-43ed-4a2d-d459-888207d45bf9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "31d64688e87848b5b582bf5c46dd4dd9",
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
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+        {
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+              "\n"
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+              "Output()"
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+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
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+        {
+          "data": {
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+        {
+          "data": {
+            "text/html": [
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "07a08a86cc2c4981beb1ff4d1715e4c0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n",
+        "# Case 12.2: Embedded statement with rdf:type as predicate\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:lara;\n",
+        "    rdf:predicate rdf:type;\n",
+        "    rdf:object ex:writer ;\n",
+        "    ex:owner ex:journal .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 167,
+          "referenced_widgets": [
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+            "a98c3bdfb31e47d5b25442fa3114f5dc",
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+            "59c050129aa24f939162cf2a24329d1f",
+            "f5486bbcd6a542029db8c33230db6fbf",
+            "00aeacee663742bca19ea1564d49593a",
+            "5f1ab0c9d552407e8dd816c32ba3ff0f",
+            "76bd81b761b0409c89b9ce656b8bee27"
+          ]
+        },
+        "id": "4TwIzKZ4S_tN",
+        "outputId": "b4f66a58-7857-411e-e900-faab135e9253"
+      },
+      "outputs": [
+        {
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+              "Output()"
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+        {
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+            ],
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+              "\n"
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+        {
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+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "
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+            ],
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5f1ab0c9d552407e8dd816c32ba3ff0f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 13: Nested RDF-star statements\n",
+        "# Case 13.1: Nested RDF-star statement in Subject position\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "ex:steve_position_ceo a rdf:Statement;\n",
+        "    rdf:subject ex:steve;\n",
+        "    rdf:predicate ex:position;\n",
+        "    rdf:object ex:CEO .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:steve_position_ceo;\n",
+        "    rdf:predicate ex:mentionedBy;\n",
+        "    rdf:object ex:book;\n",
+        "    ex:source ex:journal .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
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+        "id": "BfMbv1NpP6nt",
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+        },
+        {
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+              "
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b9f506c4c28a4e38be7b3b16cf972e06",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 13: Nested RDF-star statements\n",
+        "# Case 13.2: Nested RDF-star statements in Object positions\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "ex:steve_position_ceo a rdf:Statement;\n",
+        "    rdf:subject ex:steve;\n",
+        "    rdf:predicate ex:position;\n",
+        "    rdf:object ex:CEO .\n",
+        "\n",
+        "ex:book_mentioned_by_steve_position_ceo a rdf:Statement;\n",
+        "    rdf:subject ex:book;\n",
+        "    rdf:predicate ex:mentionedBy;\n",
+        "    rdf:object ex:steve_position_ceo .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:journal;\n",
+        "    rdf:predicate ex:source;\n",
+        "    rdf:object ex:book_mentioned_by_steve_position_ceo .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
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+            ],
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+          },
+          "metadata": {},
+          "output_type": "display_data"
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+      ],
+      "source": [
+        "# Case 14: Multi-valued properties\n",
+        "# Case 14.1: RDF statements with same subject and predicate and different objects\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "\n",
+        "ex:college_page ex:subject \"Info_Page\";\n",
+        "    ex:subject \"aau_page\" .\n",
+        "\n",
+        "ex:college_page_2 ex:subject \"Info_Page\" .\n",
+        "\n",
+        "ex:college_page ex:link ex:college_page_2 .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)\n"
+      ]
+    },
+    {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2a6a1086a40341b3a9fb46912cb75dd6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 14: Multi-valued properties\n",
+        "# Case 14.2: RDF-star statements with the same subject and predicate and different objects\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt ;\n",
+        "    ex:certainty \"0.5\"^^xsd:double .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt ;\n",
+        "    ex:certainty 1 .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 167,
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+        "id": "ZQqkFmPZQ69J",
+        "outputId": "2cc05095-f116-49e4-e2f0-deb47d721d25"
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+            "text/plain": [
+              "Output()"
+            ]
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+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
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+        },
+        {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cdd0250b550f438c863d72f4706d9131",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b2b5e1788189446fa4ecab95341c4c20",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 14: Multi-valued properties\n",
+        "# Case 14.3: Contexted within Named Graphs\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:college_page_3;\n",
+        "    rdf:predicate ex:subject;\n",
+        "    rdf:object \"Info_Page\" .\n",
+        "\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:college_page_4;\n",
+        "    rdf:predicate ex:subject;\n",
+        "    rdf:object \"Info_Page\", \"aau_page\" .\n",
+        "\n",
+        "ex:Graph1 {\n",
+        "    ex:college_page ex:subject \"Info_Page\";\n",
+        "        ex:subject \"aau_page\" .\n",
+        "}\n",
+        "\n",
+        "ex:Graph2 {\n",
+        "    [] a rdf:Statement;\n",
+        "        rdf:subject ex:college_page;\n",
+        "        rdf:predicate ex:link;\n",
+        "        rdf:object ex:college_page_3;\n",
+        "        ex:foo \"bar\";\n",
+        "        ex:foo \"bar2\" .\n",
+        "}\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
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+          "height": 167,
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+        "id": "cD-S3cZ-S_Ta",
+        "outputId": "975b43e8-0151-44e2-cee8-34c160835cc7"
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+              "Output()"
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+        {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "89fdce4ca2f94221892e9f0d1197b8e0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
+        "# Case 15.1: Basic Reified Statements\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt;\n",
+        "    ex:certainty 0.5 .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt;\n",
+        "    ex:source \"text\" .\n",
+        "\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 184,
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+        {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ab5bd1eda3b8465e8991c9dd80242069",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
+        "# Case 15.2: Basic Reified Statement + regular statement\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt;\n",
+        "    ex:certainty \"0.5\"^^xsd:double ;\n",
+        "    ex:foo ex:bar .\n",
+        "\n",
+        "ex:Mary ex:likes ex:Matt .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
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+        "id": "oWZcONtcSFwb",
+        "outputId": "16f97ebf-9a3c-4e16-dc1f-130c5a898287"
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+            "text/plain": [
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+        {
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+              "Output()"
+            ]
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+        {
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+        {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b6eed34455224c21aa7b56e9222fcf44",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
+        "# Case 15.2: Basic Reified Statement + duplicate \"regular\" statement\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt;\n",
+        "    ex:certainty \"0.5\"^^xsd:double ;\n",
+        "    ex:foo ex:bar .\n",
+        "\n",
+        "ex:Mary ex:likes ex:Matt .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 184,
+          "referenced_widgets": [
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+              "Output()"
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+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "
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+        {
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+        },
+        {
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+              "Output()"
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+          },
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+          "output_type": "display_data"
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+        {
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+        },
+        {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "44d289bcd3eb4ebd91c1e4a4b327a1e1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
+        "# Case 15.3: Basic Reified Statement + duplicate Reified Statement\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt;\n",
+        "    ex:certainty \"0.5\"^^xsd:double ;\n",
+        "    ex:foo ex:bar .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "    rdf:subject ex:Mary;\n",
+        "    rdf:predicate ex:likes;\n",
+        "    rdf:object ex:Matt .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 167,
+          "referenced_widgets": [
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+            "55577346bc664471a01442b07a9d5348"
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+        "id": "FQj1859cRwms",
+        "outputId": "a8c7c579-7cb5-4f6d-d11a-13c661411d42"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d3cf60c5428e48d19db119ff4d747021",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+          },
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+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
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+          },
+          "metadata": {},
+          "output_type": "display_data"
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+        {
+          "data": {
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+      "source": [
+        "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
+        "# Case 15.4: Nested Reified Statements with Named Graphs\n",
+        "\n",
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix xsd:  .\n",
+        "\n",
+        "[] a rdf:Statement;\n",
+        "        rdf:subject ex:Mary;\n",
+        "        rdf:predicate ex:likes;\n",
+        "        rdf:object ex:Matt;\n",
+        "        ex:certainty 1 .\n",
+        "\n",
+        "ex:Graph1 {\n",
+        "    ex:mary_likes_matt_05 a rdf:Statement;\n",
+        "        rdf:subject ex:Mary;\n",
+        "        rdf:predicate ex:likes;\n",
+        "        rdf:object ex:Matt;\n",
+        "        ex:certainty 0.5 .\n",
+        "}\n",
+        "\n",
+        "ex:Graph2 {\n",
+        "    [] a rdf:Statement;\n",
+        "        rdf:subject ex:Mary;\n",
+        "        rdf:predicate ex:likes;\n",
+        "        rdf:object ex:Matt;\n",
+        "        ex:certainty 0.75 .\n",
+        "\n",
+        "    [] a rdf:Statement;\n",
+        "        rdf:subject ex:John;\n",
+        "        rdf:predicate ex:said;\n",
+        "        rdf:object ex:mary_likes_matt_05;\n",
+        "        ex:foo \"bar\" .\n",
+        "}\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n",
+        "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0SWi4e3wIMtw"
+      },
+      "source": [
+        "#### Graph Contextualization"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vec21mb9MkhR"
+      },
+      "source": [
+        "❗ Graph Contextualiztion is a work-in-progress feature ❗\n",
+        "\n",
+        "Contextualizing an RDF Graph to enhances the Terminology Box of the original RDF Graph. This is done by:\n",
+        "\n",
+        "1. Loading the OWL, RDF, and RDFS Ontologies into the RDF Graph\n",
+        "2. Providing Domain & Range Inference\n",
+        "3. Providing Domain & Range Introspection\n"
+      ]
+    },
+    {
+      "cell_type": "code",
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+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a71eeca8fd5746eea89964c6791c3b62",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "data = \"\"\"\n",
+        "PREFIX : \n",
+        "PREFIX rdf: \n",
+        "PREFIX rdfs: \n",
+        "PREFIX xsd: \n",
+        "\n",
+        ":The_Beatles      rdf:type  :Band .\n",
+        ":The_Beatles      :name     \"The Beatles\" .\n",
+        ":The_Beatles      :member   :John_Lennon .\n",
+        ":The_Beatles      :member   :Paul_McCartney .\n",
+        ":The_Beatles      :member   :Ringo_Starr .\n",
+        ":The_Beatles      :member   :George_Harrison .\n",
+        ":John_Lennon      rdf:type  :SoloArtist .\n",
+        ":Paul_McCartney   rdf:type  :SoloArtist .\n",
+        ":Ringo_Starr      rdf:type  :SoloArtist .\n",
+        ":George_Harrison  rdf:type  :SoloArtist .\n",
+        ":Please_Please_Me rdf:type  :Album .\n",
+        ":Please_Please_Me :name     \"Please Please Me\" .\n",
+        ":Please_Please_Me :date     \"1963-03-22\"^^xsd:date .\n",
+        ":Please_Please_Me :artist   :The_Beatles .\n",
+        ":Please_Please_Me :track    :Love_Me_Do .\n",
+        ":Love_Me_Do       rdf:type  :Song .\n",
+        ":Love_Me_Do       :name     \"Love Me Do\" .\n",
+        ":Love_Me_Do       :length   125 .\n",
+        ":Love_Me_Do       :writer   :John_Lennon .\n",
+        ":Love_Me_Do       :writer   :Paul_McCartney .\n",
+        "\n",
+        ":McCartney        rdf:type  :Album .\n",
+        ":McCartney        :name     \"McCartney\" .\n",
+        ":McCartney        :date     \"1970-04-17\"^^xsd:date .\n",
+        ":McCartney        :artist   :Paul_McCartney .\n",
+        "\n",
+        ":Imagine          rdf:type  :Album .\n",
+        ":Imagine          :name     \"Imagine\" .\n",
+        ":Imagine          :date     \"1971-10-11\"^^xsd:date .\n",
+        ":Imagine          :artist   :John_Lennon .\n",
+        "\"\"\"\n",
+        "\n",
+        "rpt_contextualized_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), contextualize_graph=True, overwrite_graph=True)\n",
+        "pgt_contextualized_graph= adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), contextualize_graph=True, overwrite_graph=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9gBg-hDs77i7"
+      },
+      "source": [
+        "# ArangoDB to RDF"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KSGKrcaPa_pi"
+      },
+      "source": [
+        "We'll be using an ArangoDB-native dataset representing a Knowledge Graph constructed from the [GDETL Project](https://www.gdeltproject.org/).\n",
+        "\n",
+        "> GDELT monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day. [...] Put simply, the GDELT Project is a realtime open data global graph over human society as seen through the eyes of the world's news media, reaching deeply into local events, reaction, discourse, and emotions of the most remote corners of the world in near-realtime and making all of this available as an open data firehose to enable research over human society.\n",
+        "\n",
+        "The events we're using today range from peaceful protests to significant battles. The image below depicts the connections around an example event:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wtyqU1Wdb-jR"
+      },
+      "source": [
+        "![open_intelligence_sample copy.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0,
+          "referenced_widgets": [
+            "0806477acc4149a980271b600b0b27e6",
+            "8b258df3426f4781afed10499392c225",
+            "7145e6a71760467eb8797864f0e5d2a0",
+            "9c5380da6d794a228ac12ecab0d949ef",
+            "e6256c3b21914adc871cf4db6baf7fee",
+            "af651b267e5b4b11a2605b02f5a75ff3",
+            "f86c993cec21489983df88cee2ab5b2a",
+            "8348424c59c94f279f061d2ba339b17d",
+            "5c2e8b16eaba46d7ad63a4723a0e6c95",
+            "db764534dfec4bc3a9ef40f37c154129",
+            "ff2e1f75ed3a42e9882c2ce5e5d748c9",
+            "c4d4cc4bf5c54d6691135b25ba4d0c84",
+            "57c5c601d72d4bd8a30779500c0f63f1",
+            "678ff0b0994a40a982091b2d13f57c4a",
+            "958d1cb2fff3485a86ad5f3d9456d579",
+            "3fe5027f1c994fc4ba3c22f245604c13",
+            "050f31f028794d49bdad2e33c5e1e32e",
+            "a2954cc253fa4a2f9ad430ee12cec117",
+            "1b06375f7c054d05aac5766732801ad9",
+            "399256ab248e403ba3f26d5bd91b091c",
+            "3c19457d425b4574817572b38aa17d86",
+            "7a9a3f418d6a409cba2643daa11ec233",
+            "28ace3f50b3c4a25a7fc1b7f6eab7814",
+            "d2bc18ff98e142c58fdf18a9075d4c2b",
+            "98d22f88193c47939b93d552a3041447",
+            "66079ff3dbe043df86ba4c8cddd7ffb7",
+            "245bdf024f754fa1bbb8bedc5b59cfef",
+            "4f5490021107448cba0795253a892a71",
+            "47f69ff14b9a4021bdfe15d532a65753",
+            "3f92018b112e4213a1ee9b3d810828e5",
+            "4f4bb6f685a44d318827eecfd9d25215",
+            "a4acb870019b4b1daabbc92ce84ed88b",
+            "c48a252f55d64e78aadc927d94de4b4f",
+            "380f4a19bf51442e8bc93b77abe405d5",
+            "798d0ce58f28445cb91bcd4af75ad379",
+            "09a5785134684029b1cff1bc7510c534",
+            "f280bf45be9947bfa83e5d2ad1ffdd48",
+            "5b08d7a0cd37421fac9dd182c7e803a3",
+            "0c409a70fd6b4c42a75e575398722229",
+            "f74aa3ab9aff44f89396316e51f9774a",
+            "354a8042212b42b5ab48116c07be8c4a",
+            "ce982e0638b4403dba8bb46e8402d506",
+            "5c32f4f07b8241eb810c6b7d3bf0d516",
+            "98ef86cf824a4d7ca12d8cb76b22bbb6",
+            "772165f70bd347009bdbdc989a3610e5",
+            "e7e96edf087640c5a618deb016e4d277",
+            "9a481076f28e4e9098fca0d75d5e44f3",
+            "efcf3c8ce7424604b4726a5fdf8f9a97",
+            "ac0b18ff7a1c40b5b50326d891f5e5af",
+            "96e6a9c132cb4e068c01d819c9c53951",
+            "87ccb50738954e6d94e2d75f8194215f",
+            "096ad7b5b4844287b88f2b76e5cf107b",
+            "5e4d0d5329cd4f8f83fcef7dfd413eb1",
+            "d1ae873ccd9b459b8c8074d0cf87ff84",
+            "820e68b984f6428a8bff72a178447b41",
+            "d61489f2e3b34790905f0d2f5e32f17f",
+            "a81ff8d9fc6142188423abf029103491",
+            "ef3be25a72514885a169017b63a4212f"
+          ]
+        },
+        "id": "a6uPF7X_a2sB",
+        "outputId": "d43ae6b2-19b9-4399-9879-fd98f37f995e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0806477acc4149a980271b600b0b27e6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
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+            "text/plain": [
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+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+        {
+          "data": {
+            "text/html": [
+              "
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+              "\n"
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+          },
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+        },
+        {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Initializing collection 'subClass'\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "87ccb50738954e6d94e2d75f8194215f",
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+              "Output()"
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+        {
+          "data": {
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+              "
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+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
+          "metadata": {},
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+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Initializing collection 'type'\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "820e68b984f6428a8bff72a178447b41",
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+            "text/plain": [
+              "Output()"
+            ]
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+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
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+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
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+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n",
+        "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n",
+        "Datasets(db).load(\"OPEN_INTELLIGENCE_ANGOLA\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7mNWLGGTUipj"
+      },
+      "source": [
+        "#### via Graph Name"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
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+        "id": "rA5dBseDaiQB",
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+              "Output()"
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+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+        {
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+            "text/plain": [
+              "Output()"
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+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[2024/01/23 00:56:56 +0000] [252] [INFO] - arango_rdf: Created RDF 'OPEN_INTELLIGENCE_ANGOLA' Graph\n",
+            "INFO:arango_rdf:Created RDF 'OPEN_INTELLIGENCE_ANGOLA' Graph\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "63834\n"
+          ]
+        }
+      ],
+      "source": [
+        "rdf_graph = adbrdf.arangodb_graph_to_rdf(name=\"OPEN_INTELLIGENCE_ANGOLA\", rdf_graph=Graph())\n",
+        "\n",
+        "print(len(rdf_graph))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-FTakjQKUoWW"
+      },
+      "source": [
+        "#### via Collection Names"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
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+        "id": "TDny1v4sdelB",
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+        {
+          "data": {
+            "text/html": [
+              "
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+        {
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+            "text/plain": [
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+          },
+          "metadata": {},
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+        {
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+            "text/html": [
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+        {
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+            "text/plain": [
+              "Output()"
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+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[2024/01/23 00:57:00 +0000] [252] [INFO] - arango_rdf: Created RDF 'OPEN_INTELLIGENCE_ANGOLA' Graph\n",
+            "INFO:arango_rdf:Created RDF 'OPEN_INTELLIGENCE_ANGOLA' Graph\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "43079\n"
+          ]
+        }
+      ],
+      "source": [
+        "rdf_graph = adbrdf.arangodb_collections_to_rdf(\n",
+        "    name=\"OPEN_INTELLIGENCE_ANGOLA\",\n",
+        "    rdf_graph=Graph(),\n",
+        "    v_cols={\"Event\", \"Actor\", \"Source\"},\n",
+        "    e_cols={\"eventActor\", \"hasSource\"},\n",
+        ")\n",
+        "\n",
+        "print(len(rdf_graph))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qddfFNtaUpoo"
+      },
+      "source": [
+        "#### via MetaGraph"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0,
+          "referenced_widgets": [
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+            "fe58b0d093f24f3da92c546038fe1735",
+            "de3564cef3e44c2fbc0a25953d1b4500"
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+        },
+        "id": "kQSN13xdeH1X",
+        "outputId": "497885bc-1bdd-4a10-986f-387287b807cc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c09d4aa3bbb440eea986e872479dd746",
+              "version_major": 2,
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+            "text/plain": [
+              "Output()"
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+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
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+          },
+          "metadata": {},
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+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[2024/01/23 00:57:03 +0000] [252] [INFO] - arango_rdf: Created RDF 'OPEN_INTELLIGENCE_ANGOLA' Graph\n",
+            "INFO:arango_rdf:Created RDF 'OPEN_INTELLIGENCE_ANGOLA' Graph\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "16199\n"
+          ]
+        }
+      ],
+      "source": [
+        "rdf_graph = adbrdf.arangodb_to_rdf(\n",
+        "    name=\"OPEN_INTELLIGENCE_ANGOLA\",\n",
+        "    rdf_graph=Graph(),\n",
+        "    metagraph={\n",
+        "        \"vertexCollections\": {\n",
+        "            \"Event\": {\"date\", \"description\", \"fatalities\"},\n",
+        "            \"Actor\": {\"name\"}\n",
+        "        },\n",
+        "        \"edgeCollections\": {\n",
+        "            \"eventActor\": {}\n",
+        "        },\n",
+        "    },\n",
+        ")\n",
+        "\n",
+        "print(len(rdf_graph))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RGivoIz5Uv_-"
+      },
+      "source": [
+        "# Round-Tripping"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s900bHJQinK3",
+        "outputId": "25e0dae1-e450-4576-aed3-38d1d4223dfa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 47,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n",
+        "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n",
+        "db.delete_graph(\"OPEN_INTELLIGENCE_ANGOLA\", drop_collections=True, ignore_missing=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4PzrROxlU0TM"
+      },
+      "source": [
+        "#### RDF -> ArangoDB -> RDF"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "id": "oZNYvQxDgTvw"
+      },
+      "outputs": [],
+      "source": [
+        "data = \"\"\"\n",
+        "@prefix ex:  .\n",
+        "@prefix rdf:  .\n",
+        "@prefix rdfs:  .\n",
+        "\n",
+        "ex:Monica ex:employer ex:ArangoDB .\n",
+        "\n",
+        "ex:Graph1 {\n",
+        "    ex:Monica a ex:Entity .\n",
+        "    ex:Management a ex:Skill .\n",
+        "    ex:Monica ex:name \"Monica\" .\n",
+        "    ex:Monica ex:homepage  .\n",
+        "    ex:Monica ex:hasSkill ex:Management .\n",
+        "    ex:Monica ex:dateOfBirth \"1963-03-22\".\n",
+        "}\n",
+        "\n",
+        "ex:Graph2 {\n",
+        "    ex:Programming a ex:Skill .\n",
+        "     a ex:Website .\n",
+        "    ex:Monica a ex:Person .\n",
+        "    ex:Person rdfs:subClassOf ex:Entity .\n",
+        "    ex:Monica ex:hasSkill ex:Programming .\n",
+        "}\n",
+        "\"\"\"\n",
+        "\n",
+        "rdf_graph_original = get_graph(data, True)\n",
+        "\n",
+        "def graphs_are_identical(rdf_graph_a: Graph, rdf_graph_b: Graph) -> Graph:\n",
+        "    assert rdf_graph_a and rdf_graph_b\n",
+        "    return len(rdf_graph_a - rdf_graph_b) == 0 and len(rdf_graph_b - rdf_graph_a) == 0"
+      ]
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+        {
+          "name": "stderr",
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+          "text": [
+            "[2024/01/23 00:57:12 +0000] [252] [INFO] - arango_rdf: Created RDF 'DataPGT' Graph\n",
+            "INFO:arango_rdf:Created RDF 'DataPGT' Graph\n"
+          ]
+        }
+      ],
+      "source": [
+        "# PGT\n",
+        "adb_graph = adbrdf.rdf_to_arangodb_by_pgt(\n",
+        "    \"DataPGT\",\n",
+        "    get_graph(data, True),\n",
+        "    overwrite_graph=True,\n",
+        ")\n",
+        "\n",
+        "rdf_graph_new = adbrdf.arangodb_graph_to_rdf(\n",
+        "    \"DataPGT\",\n",
+        "    type(rdf_graph_original)()\n",
+        ")\n",
+        "\n",
+        "assert graphs_are_identical(rdf_graph_original, rdf_graph_new)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
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+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[2024/01/23 00:57:16 +0000] [252] [INFO] - arango_rdf: Created RDF 'DataRPT' Graph\n",
+            "INFO:arango_rdf:Created RDF 'DataRPT' Graph\n"
+          ]
+        }
+      ],
+      "source": [
+        "# RPT\n",
+        "adb_graph = adbrdf.rdf_to_arangodb_by_rpt(\n",
+        "    \"DataRPT\",\n",
+        "    get_graph(data, True),\n",
+        "    overwrite_graph=True,\n",
+        ")\n",
+        "\n",
+        "rdf_graph_new = adbrdf.arangodb_graph_to_rdf(\n",
+        "    \"DataRPT\",\n",
+        "    type(rdf_graph_original)()\n",
+        ")\n",
+        "\n",
+        "assert graphs_are_identical(rdf_graph_original, rdf_graph_new)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_q34B2SQU22_"
+      },
+      "source": [
+        "#### ArangoDB -> RDF -> ArangoDB"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
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+            "Initializing collection 'hasLocation'\n"
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+            "Initializing collection 'hasSource'\n"
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+            "Initializing collection 'inCountry'\n"
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+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Initializing collection 'type'\n"
+          ]
+        },
+        {
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+              "Output()"
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+              "
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+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n",
+        "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n",
+        "\n",
+        "Datasets(db).load(\"OPEN_INTELLIGENCE_ANGOLA\")\n",
+        "\n",
+        "\n",
+        "def get_adb_graph_count(name: str):\n",
+        "    global db\n",
+        "    adb_graph = db.graph(name)\n",
+        "\n",
+        "    e_cols = {col[\"edge_collection\"] for col in adb_graph.edge_definitions()}\n",
+        "\n",
+        "    v_count = 0\n",
+        "    for v in db.graph(name).vertex_collections():\n",
+        "        if v in e_cols:\n",
+        "            continue\n",
+        "\n",
+        "        v_count += adb_graph.vertex_collection(v).count()\n",
+        "\n",
+        "    e_count = 0\n",
+        "    for e_d in adb_graph.edge_definitions():\n",
+        "        e_count += adb_graph.edge_collection(e_d[\"edge_collection\"]).count()\n",
+        "\n",
+        "    return (v_count, e_count)\n",
+        "\n",
+        "original_v_count, original_e_count = get_adb_graph_count(\"OPEN_INTELLIGENCE_ANGOLA\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
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+        "# PGT\n",
+        "rdf_graph = adbrdf.arangodb_graph_to_rdf(\n",
+        "    \"OPEN_INTELLIGENCE_ANGOLA\",\n",
+        "    Graph(),\n",
+        "    list_conversion_mode=\"serialize\",\n",
+        "    dict_conversion_mode=\"serialize\",\n",
+        "    include_adb_v_col_statements=True,\n",
+        "    include_adb_v_key_statements=True,\n",
+        "    include_adb_e_key_statements=True,\n",
+        ")\n",
+        "\n",
+        "\n",
+        "adb_graph = adbrdf.rdf_to_arangodb_by_pgt(\n",
+        "    \"OPEN_INTELLIGENCE_ANGOLA\",\n",
+        "    rdf_graph,\n",
+        "    overwrite_graph=True\n",
+        ")\n",
+        "\n",
+        "property_v_count = adb_graph.vertex_collection(\"Property\").count()\n",
+        "\n",
+        "new_v_count, new_e_count = get_adb_graph_count(\"OPEN_INTELLIGENCE_ANGOLA\")\n",
+        "\n",
+        "assert original_v_count == new_v_count - property_v_count\n",
+        "assert original_e_count == new_e_count"
+      ]
+    }
+  ],
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+    "colab": {
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+        "0qry3Bcy-160",
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(RDF ↔ ADB): Extract Statements 'http://www.arangodb.com/key' ▰▱▱▱▱▱▱ 0:00:00\n
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(ADB → RDF): 'employer' ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   0% (0/1) 0:00:00\n
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(RDF → ADB): Flatten Reified Triples ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (2/2) 0:00:00\n     (RDF → ADB): Import 'likes' (2) ▰▰▱▱▱▱▱ 0:00:00\n
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(RDF → ADB): Flatten Reified Triples ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (2/2) 0:00:00\n     (RDF → ADB): Import 'DataRPT_Statement' (2) ▰▰▰▰▱▱▱ 0:00:00\n
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(RDF → ADB): PGT [RDF Literals] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   0% (1/0) 0:00:00\n     (RDF → ADB): Import 'Property' (1) ▰▰▰▰▱▱▱ 0:00:00\n
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(RDF ↔ ADB): Extract Statements 'http://www.arangodb.com/key' ▰▱▱▱▱▱▱ 0:00:00\n
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(RDF → ADB): PGT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (20/20) 0:00:00\n     (RDF → ADB): Import 'subClassOf' (8) ▰▱▱▱▱▱▱ 0:00:00\n
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RDF → ADB (RPT) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (5/5) 0:00:00\n     0:00:00 ArangoDB Import: DataRPT_URIRef ▰▱▱▱▱▱▱\n
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(RDF → ADB): Flatten Reified Triples ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (1/1) 0:00:00\n     (RDF → ADB): Import 'DataRPT_Statement' (1) ▰▰▱▱▱▱▱ 0:00:00\n
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(RDF ↔ ADB): Extract Statements 'http://www.arangodb.com/key' ▰▱▱▱▱▱▱ 0:00:00\n
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RDF → ADB (PGT) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (6/6) 0:00:00\n     0:00:00 ArangoDB Import: friend ▰▰▰▰▰▰▱\n
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RDF → ADB (RPT) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (5/5) 0:00:00\n     0:00:00 ArangoDB Import: DataRPT_Statement ▰▱▱▱▱▱▱\n
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(RDF ↔ ADB): Extract Statements 'http://www.arangodb.com/key' ▰▱▱▱▱▱▱ 0:00:00\n
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           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -12857,37 +61165,10 @@
             "width": null
           }
         },
-        "19c036100dfc4aaa8e1dd3d976a686cb": {
-          "model_module": "@jupyter-widgets/output",
-          "model_name": "OutputModel",
-          "model_module_version": "1.0.0",
-          "state": {
-            "_dom_classes": [],
-            "_model_module": "@jupyter-widgets/output",
-            "_model_module_version": "1.0.0",
-            "_model_name": "OutputModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/output",
-            "_view_module_version": "1.0.0",
-            "_view_name": "OutputView",
-            "layout": "IPY_MODEL_9ffd32b73aef47809181329a473a4cb7",
-            "msg_id": "",
-            "outputs": [
-              {
-                "output_type": "display_data",
-                "data": {
-                  "text/plain": "",
-                  "text/html": "
\n"
-                },
-                "metadata": {}
-              }
-            ]
-          }
-        },
-        "9ffd32b73aef47809181329a473a4cb7": {
+        "fe56ad08950d44ae87c2f512092edd3d": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -12936,10 +61217,37 @@
             "width": null
           }
         },
-        "13e499e94c244d22aceff1fc56f07252": {
+        "fe58b0d093f24f3da92c546038fe1735": {
           "model_module": "@jupyter-widgets/output",
+          "model_module_version": "1.0.0",
           "model_name": "OutputModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_de3564cef3e44c2fbc0a25953d1b4500",
+            "msg_id": "",
+            "outputs": [
+              {
+                "data": {
+                  "text/html": "
(ADB → RDF): 'eventActor' ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━  64% (4000/6252) 0:00:00\n
\n",
+                  "text/plain": "(ADB → RDF): 'eventActor' \u001b[38;2;94;49;8m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;94;49;8m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━\u001b[0m \u001b[35m 64%\u001b[0m (4000/6252) \u001b[33m0:00:00\u001b[0m\n"
+                },
+                "metadata": {},
+                "output_type": "display_data"
+              }
+            ]
+          }
+        },
+        "fed1fa7af3a744d6959ca223c349806b": {
+          "model_module": "@jupyter-widgets/output",
           "model_module_version": "1.0.0",
+          "model_name": "OutputModel",
           "state": {
             "_dom_classes": [],
             "_model_module": "@jupyter-widgets/output",
@@ -12949,24 +61257,24 @@
             "_view_module": "@jupyter-widgets/output",
             "_view_module_version": "1.0.0",
             "_view_name": "OutputView",
-            "layout": "IPY_MODEL_cb1d1da12c334be7b7409ae7653023d7",
+            "layout": "IPY_MODEL_6dd871296cf44b4686c58701864c723c",
             "msg_id": "",
             "outputs": [
               {
-                "output_type": "display_data",
                 "data": {
-                  "text/plain": "",
-                  "text/html": "
\n"
+                  "text/html": "
(RDF → ADB): PGT [RDF Literals] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   0% (0/0) 0:00:00\n
\n",
+                  "text/plain": "(RDF → ADB): PGT [RDF Literals] \u001b[38;2;239;125;0m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  0%\u001b[0m (0/0) \u001b[33m0:00:00\u001b[0m\n"
                 },
-                "metadata": {}
+                "metadata": {},
+                "output_type": "display_data"
               }
             ]
           }
         },
-        "cb1d1da12c334be7b7409ae7653023d7": {
+        "feebddc745d8419fb64f0d144dc5c95b": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -13015,10 +61323,37 @@
             "width": null
           }
         },
-        "4702e1b0f1e949c6991c1dfc391040d4": {
+        "ff0c11c9fd084a7790d7ae629772682f": {
           "model_module": "@jupyter-widgets/output",
+          "model_module_version": "1.0.0",
           "model_name": "OutputModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_b7e9590766f74bbdb62ea28a5bbc0ff4",
+            "msg_id": "",
+            "outputs": [
+              {
+                "data": {
+                  "text/html": "
(RDF → ADB): Flatten Reified Triples ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% (1/1) 0:00:00\n     (RDF → ADB): Import 'likes' (1) ▰▰▱▱▱▱▱ 0:00:00\n
\n",
+                  "text/plain": "(RDF → ADB): Flatten Reified Triples \u001b[38;2;255;255;255m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m (1/1) \u001b[33m0:00:00\u001b[0m\n     (RDF → ADB): Import 'likes' (1) \u001b[38;2;91;192;222m▰▰▱▱▱▱▱\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
+                },
+                "metadata": {},
+                "output_type": "display_data"
+              }
+            ]
+          }
+        },
+        "ff2e1f75ed3a42e9882c2ce5e5d748c9": {
+          "model_module": "@jupyter-widgets/output",
           "model_module_version": "1.0.0",
+          "model_name": "OutputModel",
           "state": {
             "_dom_classes": [],
             "_model_module": "@jupyter-widgets/output",
@@ -13028,24 +61363,24 @@
             "_view_module": "@jupyter-widgets/output",
             "_view_module_version": "1.0.0",
             "_view_name": "OutputView",
-            "layout": "IPY_MODEL_a8473ae663be4d8d96df2dcbe31c3f99",
+            "layout": "IPY_MODEL_c4d4cc4bf5c54d6691135b25ba4d0c84",
             "msg_id": "",
             "outputs": [
               {
-                "output_type": "display_data",
                 "data": {
-                  "text/plain": "",
-                  "text/html": "
\n"
+                  "text/html": "
GET: https://arangodb-dataset-library-ml.s3.amazonaws.com/open_intelligence_angola/vertex_collection/Country.json  \n
\n",
+                  "text/plain": "GET: https://arangodb-dataset-library-ml.s3.amazonaws.com/open_intelligence_angola/vertex_collection/Country.json  \n"
                 },
-                "metadata": {}
+                "metadata": {},
+                "output_type": "display_data"
               }
             ]
           }
         },
-        "a8473ae663be4d8d96df2dcbe31c3f99": {
+        "ffc83e4385534117bc664c291338f355": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -13099,4 +61434,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/pyproject.toml b/pyproject.toml
index 1f980d16..5bac4037 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -51,6 +51,8 @@ dev = [
     "pytest-cov>=2.0.0",
     "coveralls>=3.3.1",
     "types-setuptools",
+    "sphinx",
+    "sphinx_rtd_theme",
 ]
 
 [project.urls]