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An ontology to describe competences and human capabilities @en
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OPMV, the Open Provenance Model Vocabulary, provides terms to enable practitioners of data publishing to publish their data responsibly. @en
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Extends the Provenance Vocabulary by defining subclasses of the types of provenance elements introduced in the core ontology. @en
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The Provenance Vocabulary Core Ontology provides the main classes and properties required to describe provenance of data on the Web. @en
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This vocabulary provides supplementary terms for organisations wishing to publish open data about themselves. @en
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This version of the OSLO Exchange Standard provides a minimum set of classes and properties for describing a natural person, i.e. the individual as opposed to any role they may play in society or the relationships they have to other people, organisations and property; all of which contribute significantly to the broader concept of identity. The vocabulary is closely integrated with the Person, Organisation and Location Vocabularies published by the W3C in the Gov Linked Data Project. The OSLO specification is the result of a public-private partnership initiated by V-ICT-OR, the Flemish Organization for ICT in Local Government. @en
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PAV is a lightweight ontology for tracking Provenance, Authoring and Versioning. PAV specializes the W3C provenance ontology PROV-O in order to describe authorship, curation and digital creation of online resources. @en
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The euBusinessGraph (`ebg:`) ontology represents companies, type/status/economic classification, addresses, identifiers, company officers (e.g., directors and CEOs), and dataset offerings. It uses `schema:domainIncludes/rangeIncludes` (which are polymorphic) to describe which properties are applicable to a class, rather than `rdfs:domain/range` (which are monomorphic) to prescribe what classes must be applied to each node using a property. We find that this enables more flexible reuse and combination of different ontologies. We reuse the following ontologies and nomenclatures, and extend them where appropriate with classes and properties: - W3C Org, W3C RegOrg (basic company data), - W3C Time (officer membership), - W3C Locn (addresses), - schema.org (domain/rangeIncludes and various properties) - DBpedia ontology (jurisdiction) - NGEO and Spatial (NUTS administrative divisions) - ADMS (identifiers), - FOAF, SIOC (blog posts), - RAMON, SKOS (NACE economic classifications and various nomenclatures), - VOID (dataset descriptions). This is only a reference. See more detail in the [EBG Semantic Model](https://docs.google.com/document/d/1dhMOTlIOC6dOK_jksJRX0CB-GIRoiYY6fWtCnZArUhU/edit) google document, which includes an informative description of classes and properties, gives examples and data provider rules, and provides more schema and instance diagrams. @en
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The Cochrane Core ontology describes the entities and concepts that exist in the domain of evidence based healthcare. It is used for the construction of the Cochrane Linked Data Vocabulary containing some 400k terms including Interventions (Drugs, Procedures etc), Populations (Age, Sex, Condition), and clinical Outcomes. @en
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The PICO ontology provides a machine accessible version of the PICO framework. It essentially provides a model for describing evidence in a consistent way. The model allows the specifying of complex populations, detailed interventions and their comparisons as well as the outcomes considered. The PICO ontology was originally designed to model the questions asked and answered in Cochrane's systematic reviews. As a leader in the field of evidence based healthcare Cochrane uses the PICO model when framing and publishing evidence based questions. The PICO model is widely adopted for describing healthcare evidence, furthermore is equally applicable in other evidence-based domains. It essentially provides a model for describing evidence in a consistent way. @en
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The Semantic Web Conference ontology (SWC) is an ontology for describing academic conferences @en
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The Vocabulary of Dataset Publication Projects (VDPP) allows to represent the status of a dataset publication project. It is mainly based on the Provenance Vocabulary (PRV), the Dataset Provenance Vocabulary (VOIDP), the Vocabulary of Interlinked Datasets (VoID), and the Description of a Project (DOAP) vocabulary. @en
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Person ontology in Dutch @en
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An OWL representation of parts of the Geographic Metadata model described in ISO 19115:2003 with Corrigendum 2006 - DQ Package @en
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The Knowledge Diversity Ontology aims at providing a vocabulary that describes different dimensions of knowledge diversity of the Web. To support the representation of diversity information, the conceptual model of the Knowledge Diversity Ontology includes concepts and relations that were identified and modelled by focusing on real world scenarios in context of customer feedback, news, and Wikipedia opinion mining as well as content and sentiment analysis. @en