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semanticnet

semanticnet is a small python library to create semantic graphs in JSON. Those created datasets can then be visualized with the 3D graph engine.

A quick example

A quick example

To generate and save the graph represented by this image, you would write

import semanticnet as sn

g = sn.Graph()

a = g.add_node({ "label" : "A" })
b = g.add_node({ "label" : "B" })
c = g.add_node({ "label" : "C" })

g.add_edge(a, b, { "type" : "belongs" })
g.add_edge(b, c, { "type" : "owns" })
g.add_edge(c, a, { "type" : "has" })

g.save_json("output.json")

which would save the graph to a file output.json, which could be used by OpenGraphiti.

There are several other example scripts included in this repo to demonstrate usage of SemanticNet. Each example is documented in the wiki.

JSON representation

When saving graph objects as JSON, the graph is represented internally as one might expect. Suppose you have a graph G = (V, E), where

V = {0, 1, 2} and E = {(0, 1), (0, 2), (1, 2)}

Suppose further that:

  1. Vertex 0 has the attributes: {"type": "A", "id": 0}
  2. Vertex 1 has the attributes: {"type": "B", "id": 1}
  3. Vertex 2 has the attributes: {"type": "C", "id": 2}
  4. Edge (0, 1) has the attributes: {'src': 0, 'dst': 1, 'type': 'normal', 'id': 0}
  5. Edge (0, 2) has the attributes: {'src': 0, 'dst': 2, 'type': 'normal', 'id': 1}
  6. Edge (1, 2) has the attributes: {'src': 1, 'dst': 2, 'type': 'irregular', 'id': 1}

then in JSON format, it would look like:

{
 "timeline": [], 
 "nodes": [
  {
   "type": "A", 
   "id": 0
  }, 
  {
   "type": "B", 
   "id": 1
  }, 
  {
   "type": "C", 
   "id": 2
  }
 ], 
 "meta": {}, 
 "edges": [
  {
   "src": 0, 
   "dst": 1, 
   "type": "normal", 
   "id": 0
  }, 
  {
   "src": 0, 
   "dst": 2, 
   "type": "normal", 
   "id": 1
  }, 
  {
   "src": 1, 
   "dst": 2, 
   "type": "irregular", 
   "id": 2
  }
 ]
}

As you can see, there is a list of "node" objects, each of which contain the node's attributes and IDs, as well as a list of "edge" objects, each of which have the edge's attributes, and the fields "src" and "dst", which indicate the source and destination vertices, respectively.

Without user definition, the "id" fields will default to randomly-generated UUIDs, although they can be any hashable type.

Caching

Should you come across a use case where you'd like quick references to nodes or edges by more than just the ID, semanticnet provides a mechanism to cache nodes and edges by any of their attributes. For example, suppose you make the following graph:

>>> g = sn.Graph()
>>> a = g.add_node({"type": "server"})
>>> b = g.add_node({"type": "server"})
>>> c = g.add_node({"type": "client"})
>>> g.add_edge(a, b, {"method": "GET", "port": 80})
UUID('eeb41fd0-9229-47eb-84f0-08ae37a341b2')
>>> g.add_edge(a, c, {"method": "GET", "port": 80})
UUID('d490157e-621f-4e4d-ba93-68e83f3230dc')
>>> g.add_edge(b, c, {"method": "POST", "port": 443})
UUID('9b2bcaf3-7af7-45a4-871e-d453e1ae8f2c')

Suppose further that you want to access the nodes by their "type" attribute. You can tell semanticnet to cache the nodes by the "type" attribute, and access them like so:

>>> g.cache_nodes_by("type")
>>> g.get_nodes_by_attr("type")
{'client': [{'type': 'client', 'id': UUID('8ccbcf75-603e-4a53-83a8-ccb0c4680f57')}], 'server': [{'type': 'server', 'id': UUID('125eb4a5-705f-420d-839c-59f15f2238d5')}, {'type': 'server', 'id': UUID('df0ac3ba-920d-4c46-9da8-748cf17b7e45')}]}

Similarly, you could get a list of all connections by port:

>>> g.cache_edges_by("port")
>>> g.get_edges_by_attr("port")
{80: [{'port': 80, 'src': UUID('df0ac3ba-920d-4c46-9da8-748cf17b7e45'), 'dst': UUID('8ccbcf75-603e-4a53-83a8-ccb0c4680f57'), 'id': UUID('d490157e-621f-4e4d-ba93-68e83f3230dc'), 'method': 'GET'}, {'port': 80, 'src': UUID('df0ac3ba-920d-4c46-9da8-748cf17b7e45'), 'dst': UUID('125eb4a5-705f-420d-839c-59f15f2238d5'), 'id': UUID('eeb41fd0-9229-47eb-84f0-08ae37a341b2'), 'method': 'GET'}], 443: [{'port': 443, 'src': UUID('125eb4a5-705f-420d-839c-59f15f2238d5'), 'dst': UUID('8ccbcf75-603e-4a53-83a8-ccb0c4680f57'), 'id': UUID('9b2bcaf3-7af7-45a4-871e-d453e1ae8f2c'), 'method': 'POST'}]}

and you can specify the attribute value as well, to return the list of connections by, say, port 80:

>>> g.get_edges_by_attr("port", 80)
[{'port': 80, 'src': UUID('df0ac3ba-920d-4c46-9da8-748cf17b7e45'), 'dst': UUID('8ccbcf75-603e-4a53-83a8-ccb0c4680f57'), 'id': UUID('d490157e-621f-4e4d-ba93-68e83f3230dc'), 'method': 'GET'}, {'port': 80, 'src': UUID('df0ac3ba-920d-4c46-9da8-748cf17b7e45'), 'dst': UUID('125eb4a5-705f-420d-839c-59f15f2238d5'), 'id': UUID('eeb41fd0-9229-47eb-84f0-08ae37a341b2'), 'method': 'GET'}]

The cache is managed automatically. Any time you add or remove a node/edge with an attribute that you are caching, or modify an attribute of a node/edge, semanticnet updates the cache.

Installation

To install, you can simply run

pip install semanticnet

Manual installation

git clone https://github.com/ThibaultReuille/semanticnet.git
cd semanticnet
./setup.py install

Tests

If you wish to run the test suite, it uses py.test. Install it with:

pip install pytest

and run the tests with:

py.test -v ./test