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Ontologies
This content ontology design patterns represents plans descriptions and their executions. It is defined by combining and expanding other CPs: basic plans description, basic plans execution, and object role. Expansion involves the partial clone of ontology elements from DOLCE Ultra Lite and Plans Lite ontologies.
The CIDOC Conceptual Reference Model (CRM) is a theoretical and practical tool for information integration in the field of cultural heritage. It can help researchers, administrators and the public explore complex questions with regards to our past across diverse and dispersed datasets. The CIDOC CRM achieves this by providing definitions and a formal structure for describing the implicit and explicit concepts and relationships used in cultural heritage documentation and of general interest for the querying and exploration of such data. Such models are also known as formal ontologies. These formal descriptions allow the integration of data from multiple sources in a software and schema agnostic fashion.
The CIDOC CRM has been developed in a manner that is intended to promote a shared understanding of cultural heritage information by providing a common and extensible semantic framework for evidence-based cultural heritage information integration. It is intended to be a common language for domain experts and implementers to formulate requirements for information systems and to serve as a guide for good practice of conceptual modelling. In this way, it can provide the "semantic glue" needed to mediate between different sources of cultural heritage information, such as that published by museums, libraries and archives.
This ontology intends to represent control flows: activation, branching, decisions, concurrency, etc.
The Descriptions and Situations ontology (DnS) is based on the distinction peculiar to philosophy between flux, the flow of events in history, and logos, the human discourse about them, the “intentionality”.
DOLCE is a foundational ontology developed and maintained by the ISTC-CNR Laboratory for Applied Ontology . It was originally developed within the WonderWeb project and was conceived as the first module of the WonderWeb Foundational Ontologies Library (WFOL) together with OCHRE and BFO. DOLCE has remained stable after its first release in 2002/2003. It is formally specified in first-order logic (FOL); its consistency has been proved by Oliver Kutz and Till Mossakowski.
Pervasive in old and new media, and relevant from a multi-disciplinary perspective that ranges from literary criticism and semiotics to aesthetics and psychology, the manifestations of drama are ubiquitous in today’s culture, overcoming the boundaries of genres and formats. Drawing from an extensive survey of the literature on drama, Drammar formalizes the elements of drama in a media- and task-independent way. Drammar is encoded in OWL2 to provide an interoperable formal model for representing and studying drama in a variety of systems. Issued by a research initiative carried out in an interdisciplinary way over a decade, Drammar abstracts from the media and languages by which drama is conveyed, providing a formal systematization of the core notions about drama that leverages the theories and models of agency in the tradition of Artificial Intelligence. In this paper, we illustrate the ontology, describing its design and motivations through examples and use cases.
The original Dublin Core™ of thirteen (later fifteen) elements was first published in the report of a workshop in 1995. In 1998, this was formalized in the Internet Engineering Task Force standard RFC 5791, and discussions began about making it a standard of the (US) National Information Standards Organization (NISO). The European Committee for Standardization (CEN) published the Dublin Core Metadata Element Set as CWA 13874. The element set was then published as the US standard ANSI/NISO Z39.85-2001 and as an international standard, ISO 15836-2003. The most recent updates of these standards are RFC 5791 (2010), Z39-85-2012, and ISO 15836-1:2017. ISO 15836 Part 2, covering several dozen properties and classes that have been added to DCMI namespaces since 1999, was published in 2019.
Starting in 2002, DCMI grew into the role of "de facto" standards agency by maintaining its own, updated documentation for DCMI Metadata Terms. The DCMI Usage Board currently serves as the maintenance agency for ISO 15836.
In addition to these semantic specifications, DCMI working groups have developed specifications on other topics of relevance to metadata, such as encoding syntaxes, usage guidelines, and metadata models. In the rapidly evolving environment of the World Wide Web, most of these specifications have been superseded over time, sometimes after influencing subsequent work by other technical communities, notably the World Wide Web Consortium.
A UK project jointly between industry and academics to use knowledge-based systems in enterprise modelling and the strategic use of IT to help manage change. It supports the use of enterprise modelling methods which capture various aspects of how a business works and how it is organised. The Enterprise Ontology was created during this project and includes process and plan representations in the context of an enterprise. An Ontolingua encoding was made available.
EVO aims to propose a model to represent historical events and a tool to encode them using the W3C standards for publishing data on the Semantic Web. The model defines events in relation to agents, locations, time, cause and consequence and aligns with CIDOC-CRM, the authoritative upper ontology for the domain of cultural heritage, which, in fact, is far too complex to use and not entirely suited for the representation of historical events. The objective was to propose a tool-assisted method on how to organize and represent this important domain. To achieve this, we combined insights from terminology work as defined by the latest version of ISO 1087 and A.I. models of representing descriptive knowledge concerning the objects of the domain. Our contention is that this novel approach has a lot to offer to diverse communities of scholars and practitioners in Digital Humanities and Ontology Engineering in need of tool to publish the term in a particular domain both as human-readable ontology-based dictionaries and machine-tractable Semantic web compliant ontologies.
The Function Ontology provides a way to semantically declare and describe implementation-independent functions, and their relations to related concepts such as parameters, outputs, related problems, algorithms, mappings to concrete implementations, and executions.
A list of the publications concerning the Function Ontology can be viewed at https://fno.io/.
The main scientific publication is Implementation-independent function reuse [10.1016/j.future.2019.10.006].
The multi metamodel process ontology (m3po) combines workflows and choreography descriptions so that it can be used as a process interchange ontology. For internal business processes, Workflow Management Systems are used for process modelling and allow describing and executing business processes. For external business processes, choreography descriptions are used to describe how business partners can cooperate. A choreography can be considered to be a view of an internal business process with the internal logic not visible, similar to public views on private workflows. The m3po ontology unifies both internal and external business processes, combining reference models and languages from the workflow and choreography domains. The m3po ontology is written in WSML. The related ontology m3pl, written in PSL using the extension FLOWS (First Order Logic for Web Services), enables the extraction of choreography interfaces from workflow models.
The Ontology for Media Resources 1.0 describes a core vocabulary of properties and a set of mappings between different metadata formats of media resources hat describe media resources published on the Web (as opposed to local archives, museums, or other non-web related and non-shared collections of media resources). The purpose of these mappings is to provide metadata representations that describe the characteristics and behavior of media resources in an interoperable manner, thereby enabling different applications to share and reuse these metadata.
Digital Libraries (DLs), especially in the Cultural Heritage domain, are rich in narratives. Every digital object in a DL tells some kind of story, regardless of the medium, the genre, or the type of the object. However, DLs do not offer services about narratives, for example it is not possible to discover a narrative, to create one, or to compare two narratives. Certainly, DLs offer discovery functionalities over their contents, but these services merely address the objects that carry the narratives (e.g. books, images, audiovisual objects), without regard for the narratives themselves. The present work aims at introducing narratives as first-class citizens in DLs, by providing a formal expression of what a narrative is. In particular, this paper presents a conceptualization of the domain of narratives, and its specification through the Narrative Ontology (NOnt for short), expressed in first-order logic. NOnt has been implemented as an extension of three standard vocabularies, i.e. the CIDOC CRM, FRBRoo, and OWL Time, and using the SWRL rule language to express the axioms. An initial validation of NOnt has been performed in the context of the Mingei European project, in which the ontology has been applied to the representation of knowledge about Craft Heritage.
The US National Institute of Standard and Technology (NIST) worked with groups from manufacturing, process Engineering and Artificial Intelligence to formulate standards for process modelling and interchange. More than 20 candidate models and previous relevant work was input. The NIST PSL effort was adopted as the ISO 18629 standard for industrial automation systems and integration - process specification language.
The Plans module includes concepts and relations for modelling plans, tasks, roles, executions, etc., as a plugin to the DOLite+ library.
The PROV Ontology (PROV-O) expresses the PROV Data Model [PROV-DM] using the OWL2 Web Ontology Language (OWL2) [OWL2-OVERVIEW]. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains. The PROV Document Overview describes the overall state of PROV, and should be read before other PROV documents.
The Publishing Workflow Ontology (PWO) is a simple ontology for describing the steps in the workflow associated with the publication of a document or other publication entity.
Reasonable Ontology Templates (OTTR) is a language with supporting tools for representing and instantiating RDF graph and OWL ontology modelling patterns. It is designed to improve the efficiency and quality of building, using, and maintaining knowledge bases.
The Semantic Web Services Ontology (SWSO) is expressed in two forms: FLOWS, the First-order Logic Ontology for Web Services; and ROWS, the Rules Ontology for Web Services, produced by a systematic translation of FLOWS axioms into the SWSL-Rules language. FLOWS has been specified in SWSL-FOL, the first-order logic language developed by SWSO's sister SWSL effort.
The project SUPER (Semantics Utilised for Process management within and between EnteRprises) has a goal of the definition of ontologies for Semantic Business Process Management (SBPM), but these ontologies can be reused in diverse environments. Part of this project is to define an Upper Process Ontology (UPO) that ties together all other SUPER ontologies. The results of the project SUPER include the UPO and a set of ontologies for processes and organizations. Most of the ontologies are written in WSML, and some are also written in OCML.
The Defense Advanced Research Projects Agency (DARPA) and US Air Force Research Laboratory Planning Initiative (ARPI) project to draw on the range of previous work in planning and activity ontologies to create a practically useful "Shared Planning and Activity Representation" - SPAR - for use in technology and applications projects within their communities. In addition to the past ARPI research efforts and experiences related to building shared plan representations (e.g. KRSL, KRSL-Plans, PIF, OMWG CPR) this work also intended to draw on relevant pre-standards research including work on representing and interchanging process knowledge within and across enterprises.
SPAR is based on the assumption that it is important that information about processes, plans and activities is able to be shared within and across organisations. Cooperation and coordination of the planning, monitoring and workflows of the organisations can be assisted by having a clear shared model of what comprises plans, processes and activities. The Shared Planning and Activity Representation is intended to contribute to a range of purposes including domain modelling, plan generation, plan analysis, plan case capture, plan communication and behaviour modelling. By having a shared model of what constitutes a plan, process or activity, organisational knowledge can be harnessed and used effectively.
The SPIN Modeling Vocabulary is a light-weight collection of RDF properties and classes to support the use of SPARQL to specify rules and logical constraints. Based on an RDF representation of SPARQL queries, SPIN defines three class description properties: spin:constraint can be used to define conditions that all members of a class must fulfill. spin:rule can be used to specify inference rules using SPARQL CONSTRUCTs and DELETE/INSERTs. spin:constructor can be used to initialize new instances with default values. In addition to these class description properties, SPIN provides a powerful meta-modeling capability that can be used to build your own modeling language and SPARQL extensions. These meta-modeling features provide the ability to encapsulate reusable SPARQL queries into templates, and to derive new SPARQL functions as well as magic properties from other SPARQL queries and functions.
The potential to achieve dynamic, scalable and cost-effective infrastructure for electronic transactions in business and public administration has driven recent research efforts towards so-called Semantic Web services, that is enriching Web services with machine-processable semantics. Supporting this goal, the Web Service Modeling Ontology (WSMO) provides a conceptual framework and a formal language for semantically describing all relevant aspects of Web services in order to facilitate the automation of discovering, combining and invoking electronic services over the Web.
The workflow ontology is used to capture both sequential and state-based workflows. A workflow is an instantiation of the Workflow class (or subclass) in the ontology. The ontology captures the structure of the workflow - using classes, and a specific workflow - in the instances, e.g. a voting workflow. When used, we need another layer to capture an executable workflow, e.g., the voting workflow for user 1 when in state 3.