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The GVP Ontology defines classes, properties and values (skos:Concepts) used in GVP LOD. It is complete regarding AAT and TGN (as of version 2.0), and will be extended in time with more elements needed for the other GVP vocabularies (ULAN, CONA). @en
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An ontology to capture the staggering diversity of polymeric materials and their applications. @en
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This ontology provides the base elements required by myExperiment for content management, social networking and object annotation. @en
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The SALT Document Ontology captures the linear structure of the publication, in addition to the identification and revisioning information of the publication's content. @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 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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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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This is the Materials Design Ontology. @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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The Evaluation ontology describes evaluation of [`seas:Property`ies](https://w3id.org/seas/Property). There may be: - direct evaluations, or - qualified evaluations. @en
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The SEAS Forecasting ontology extends the [Procedure Execution ontology (PEP)](https://w3id.org/pep/) @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
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Extends owl-time ontology with support for several timelines, acting as a backbone to adress time interval/instants. Mainly designed with a multimedia use-case in mind. @en
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Ontology for healthcare metadata - especially metadata found in DICOM files (Digital Imaging and Communications in Medicine, see http://dicom.nema.org/). Author: Michael Brunnbauer, Bonubase GmbH (www.bonubase.com). The author's email address is brunni@netestate.de. See http://purl.org/healthcarevocab/v1help for explanations. @en
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This ontology is being developed by CSIRO under the eReefs project for describing data provider nodes, web services available and datasets that are hosted by them. This ontology features a module for describing Datasets. It does not however describe geospatial, temporal, organisational or domain concepts as these are intended to be included from other ontologies via the imports statement. Other modules complementary to the DPN ontology are http://purl.org/dpn/dataset and http://purl.org/dpn/services. This version aligns DCAT and DC terms and imports DPN services. @en