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The basic participation pattern, without temporal indexing. It clones equivalent elements from DOLCE-UltraLite. @en
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This ontology models and represents vCards in RDF using current best practices @en
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The Translational Medicine Ontology (TMO) is a high-level, patient-centric ontology that extends existing domain ontologies to integrate data across aspects of drug discovery and clinical practice. The ontology has been developed by participants in the World Wide Web Consortium's Semantic Web for Health Care and Life Sciences Interest Group @en
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GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en
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AIRO represents AI risk concepts and relations based on the AI Act draft and ISO 31000 standard series. @en
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The Core module represents general-purpose concepts orthogonal to the whole network, which are imported by all other ontology modules (e.g. part-whole relation, classification). @en
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The Construction Tasks Ontology (CTO) describes tasks operating on construction elements, spatial zones and/or damages. The tasks are either planned or executed depending on the dataset metadata context of the dataset its used in. Five different types of tasks are defined: instalment, removal, modification, repair and inspection. Consequences of tasks on the dataset, i.e. added and/or deleted triples, are modeled using reified statements. The tasks can link to a reified statement using the CTO relations. @en
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An ontology for describing changes between OWL ontology versions @en
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Ontology that defines the topology of damages in constructions. @en
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The BDI Ontology provides a formal framework to model the Belief-Desire-Intention (BDI) architecture for rational agents. It defines key mental states—Beliefs, Desires, and Intentions—and their relationships, capturing the agent’s reasoning, motivation, and commitment to action. Supporting classes include Propositions (content of mental states), Justifications (rationale for mental states), Plans (action sequences for goals), and TimeIntervals (temporal validity of entities). Key properties like hasBelief, hasDesire, and hasIntention link agents to mental states, while fulfills, adoptsIntention, and motivatesDesire model dynamic interactions. Temporal properties enable reasoning about time-sensitive states and plans. Axioms ensure consistency, such as disjointness between mental states and domain-specific constraints. This ontology supports reasoning, querying, and analysis of agent behaviour, enabling applications in AI, multi-agent systems, and decision support. @en
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The NORIA-O project is a data model for IT networks, events and operations information. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing an IT Service Management (ITSM) Knowledge Graph (KG) for Anomaly Detection (AD) and Risk Management applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g. with `rdfs:subClassOf`, `owl:equivalentClass`). Directions for direct instanciation of these vocabularies are provided for cases where implementing a class/property alignment is redundant. Alignment holds for the following vocabulary releases: - [BBO](https://hal.archives-ouvertes.fr/hal-02365012/) 1.0.0 - [BOT](https://w3id.org/bot/) 0.3.2 - [DevOps-Infra](https://oeg-upm.github.io/devops-infra/) 1.0.0 - [FOLIO](https://github.com/IBCNServices/Folio-Ontology) 1.0.0 - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [PEP](https://w3id.org/pep/) 1.1 - [SEAS](https://w3id.org/seas/) 1.1 - [SLOGERT](https://sepses.ifs.tuwien.ac.at/ns/log/index-en.html) 1.1.0 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @en
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This is the Materials Design Ontology. @en
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The Procedural Knowledge Ontology (PKO) addresses the Procedural Knowledge (PK) domain, and models procedures, their executions, and related resources and agents. @en
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## RDF Presentation and RDF Presentation Negotiation An RDF graph can be presented in several ways, using different media types. Examples of RDF media types include `application/rdf+xml`, `text/turtle`, `application/json+ld`. Today, most of the content consumed/produced/published, on the Web is not presented in RDF. In the Web of Things, HTTP servers and clients would rather exchange lightweight documents, potentially binary. Currently, most existing RDF Presentations generically apply to any RDF graph, at the cost of being heavy text-based documents. Yet, lightweight HTTP servers/clients could be better satisfied with consuming/producing/publishing lightweight documents, may its structure be application-specific. @en
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The System Ontology defines Systems, Connections between systems, and Connection Points at which systems may be connected. This ontology is then specialized for multiple domains. For example: - In electric energy: - power systems consume, produce, store, and exchange electricity; - power connections are where electricity flows between systems; - power connection points are plugs, sockets, or power busses. - In the electricity market: - players and markets are systems; - connections are contracts or transactions between two players, or between a player and a market; - connection points include offers and bids. @en