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This document specifies a vocabulary for describing an IBIS (issue-based information system). @en
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This ontology models personalized tourist experiences by representing cities, points of interest, events, accommodations, restaurants, transportation, and their relationships. This ontology is part of a university project. @en
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The development of the SAREF4GRID ontology has been partially funded by the IA4TES project (MIA.2021.M04.0008), funded by the Spanish Ministry of Economic Affairs and Digital Transformation and by the NextGenerationEU program @en
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GConsent provides concepts and relationships for defining consent and its associated information or metadata with a view towards GDPR compliance. It is the outcome of an analysis of consent and requirements associated with obtaining, using, and changes in consent as per the GDPR. The ontology also provides an approach to using its terms in various scenarios and use-cases (see more information in the documentation) which is intended to assist in its adoption. @en
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A vocabulary to represent relations that should be more transparent, usually between powerfull people or institutions @en
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The General Data Protection Regulation (GDPR) is comprised of several articles, each with points that refer to specific concepts. The general convention of referring to these points and concepts is to quote the specific article or point using a human-readable reference. This ontology provides a way to refer to the points within the GDPR using the EurLex ontology published by the European Publication Office. It also defines the concepts defined, mentioned, and requried by the GDPR using the Simple Knowledge Organization System (SKOS) ontology. @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 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 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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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 FAIR Assessment Ontology is designed to model the process and outcomes of evaluating digital objects according to the FAIR principles. It captures FAIR assessments as provenance-aware activities, linking evaluated resources to detailed results for each FAIR sub-principle. Each assessment can include multiple sub-principle results, associated evidence, quantitative scores, and test outcomes (e.g., pass, fail, indeterminate). The ontology also models the computational aspects of FAIR evaluation, including scoring functions, calculation algorithms, and aggregation methods used to derive higher-level scores. The ontology is aligned with the FAIR Vocabulary (https://w3id.org/fair/principles/terms/) for the definition of principles and sub-principles, and reuses established standards such as PROV-O for provenance and SKOS for controlled vocabularies. This ensures interoperability with existing FAIR tools and supports integration into automated FAIR assessment pipelines. @en