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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 Context Description module includes models for the context of a cultural property, in a broad sense: agents (e.g.: author, collector, copyright holder), objects (e.g.: inventories, bibliography, protective measures, other cultural properties, collections etc.), activities (e.g.: surveys, conservation interventions), situations (e.g.: commission, coin issuance, estimate, legal situation) related, involved or involving the cultural property. Thus it represents attributes that do not result from a measurement of features in a cultural property, but are associated with it. @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 Denotative Description module encodes the characteristics of a cultural property, as detectable and/or detected during the cataloguing process and measurable according to a reference system. Examples include measurements e.g. length, constituting materials e.g. clay, employed techniques e.g. melting, conservation status e.g. good, decent, bad. In this module are used as template the following Ontology Design Patterns: - http://www.ontologydesignpatterns.org/cp/owl/collectionentity.owl - http://www.ontologydesignpatterns.org/cp/owl/classification.owl - http://www.ontologydesignpatterns.org/cp/owl/descriptionandsituation.owl - http://www.ontologydesignpatterns.org/cp/owl/situation.owl @en
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A vocabulary to describe a person's profile and history @en
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The Data Knowledge Vocabulary allows for a comprehensive description of data assets and enterprise data management. It covers a business data dictionary, data quality management, data governance, the technical infrastructure and many other aspects of enterprise data management. The vocabulary represents a linked data implementation of the Data Knowledge Model which resulted from extensive applied research. @en
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A Knowledge Model to describe a smart city, that interconnect data from infomobility service, Open Data and other source @en
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This ontology describes a person character as a vector of demographic traits, each dimension refers to a concept contained within a specific taxonomy or to an instance of a wikidata item. @en
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The Data Privacy Vocabulary (DPV) provides terms (classes and properties) to represent information about processing of personal data, for example - purposes, processing operations, personal data, technical and organisational measures. @en
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Extension to the Data Privacy Vocabulary (DPV) providing additional categories of personal data @en
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The ontology of agent relationships, AgRelOn, defines relations of persons to other persons and to organisations @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 ontology of the taxonomy "European Skills, Competences, qualifications and Occupations". The ontology considers three ESCO pillars (or taxonomy) and 2 registers. The three pillars are: - Occupation - Skill (and competences) - Qualification For the construction and use of the ESCO pillars, the following modelling artefacts are used: - Facetting support to specialize ESCO pillar concepts based on bussiness relevant Concept Groups (e.g. species, languages, ...) - Conept Groups, Thesaurus array and Compound terms (as detailed in ISO 25964) to organize faceted concepts - SKOS mapping properties to relate ESCO pillar concepts to concepts in other (external) taxonomies (e.g. FoET, ISCO88 and ISCO08. More mappings can be added in the future.) - Tagging ESCO pillar concepts by other (external) taxonomies (NUTS, EQF, NACE, ...) - Capture gender specifics on the labels of the ESCO pillar concepts - Rich ESCO concept relationships holding a description and other specific characteristics of the relation between two ESCO pillar concepts. ESCO maintains two additional registers: - Awarding Body - Work Context Awarding Bodies typically are referenced by ESCO qualifications. Occupations can have one or more work context. @en