82
results
-
The Data Quality Vocabulary (DQV) is seen as an extension to DCAT to cover the quality of the data, how frequently is it updated, whether it accepts user corrections, persistence commitments etc. When used by publishers, this vocabulary will foster trust in the data amongst developers. @en
-
The Dataset Usage Vocabulary (DUV) is used to describe consumer experiences, citations, and feedback about datasets from the human perspective. @en
-
EARL is a vocabulary, the terms of which are defined across a set of specifications and technical notes, and that is used to describe test results. The primary motivation for developing this vocabulary is to facilitate the exchange of test results between Web accessibility evaluation tools in a vendor-neutral and platform-independent format. It also provides reusable terms for generic quality assurance and validation purposes. @en
-
The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, annotations, using a methodology that conforms to the Architecture of the World Wide Web. This ontology is a non-normative OWL formalization of the textual OA specification at http://www.openannotation.org/spec/core/20130208/index.html @en
-
The Registered Organization Vocabulary is a profile of the Organization Ontology for describing organizations that have gained legal entity status through a formal registration process, typically in a national or regional register. @en
-
This vocabulary defines terms used in SHACL, the W3C Shapes Constraint Language. @en
-
Web Of Trust (wot) RDF vocabulary, described using W3C RDF Schema and the Web Ontology Language. @en
-
The ontology is aimed at the support of research groups in the field of Business Modeling and Knowledge Engineering (BMaKE) in their collaborative work for qualitatively analyzing scholarly papers as well as sharing the results of that analyses and judgements. @en
-
This document specifies a vocabulary for describing an IBIS (issue-based information system). @en
-
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
-
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
-
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
-
GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en
-
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
-
AIRO represents AI risk concepts and relations based on the AI Act draft and ISO 31000 standard series. @en