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  • cert - The Cert Ontology
    http://www.w3.org/ns/auth/cert#
    Ontology for Certificates and crypto stuff. @en
  • sh - W3C Shapes Constraint Language (SHACL) Vocabulary
    http://www.w3.org/ns/shacl#
    This vocabulary defines terms used in SHACL, the W3C Shapes Constraint Language. @en
  • locn - ISA Programme Location Core Vocabulary
    http://www.w3.org/ns/locn
    The ISA Programme Location Core Vocabulary provides a minimum set of classes and properties for describing any place in terms of its name, address or geometry. The vocabulary is specifically designed to aid the publication of data that is interoperable with EU INSPIRE Directive. @en
  • oa - Open Annotation Data Model
    http://www.w3.org/ns/oa#
    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
  • wot - Web Of Trust
    http://xmlns.com/wot/0.1/
    Web Of Trust (wot) RDF vocabulary, described using W3C RDF Schema and the Web Ontology Language. @en
  • spvqa - Scholarly Papers Vocabulary with Focus on Qualtitative Analysis
    https://bmake.th-brandenburg.de/spv
    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
  • atts - Air Traffic Temporal and Spacial Vocabulary
    https://data.nasa.gov/ontologies/atmonto/general#
    Defines temporal / spatial concepts and general-purpose datastructures @en
  • hht - Historical Hierarchical Territories
    https://w3id.org/HHT
    The notion of territory plays a major role in human and social sciences. In an historical context, most approaches are irrelevant as they rely on geometric data, which is not available. In order to represent historical territories,we conceived the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. This approach relies on a notion of building blocks to replace polygonal geometry @en
  • gdprov - The GDPR Provenance ontology
    https://w3id.org/GDPRov
    GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en
  • airo - AI Risk Ontology
    https://w3id.org/airo
    AIRO represents AI risk concepts and relations based on the AI Act draft and ISO 31000 standard series. @en
  • a-loc - Location Ontology (ArCo network)
    https://w3id.org/arco/ontology/location
    The module Location models information related to the localization and georeferencing of a cultural property. 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/place.owl - http://www.ontologydesignpatterns.org/cp/owl/timeindexedsituation.owl - http://www.ontologydesignpatterns.org/cp/owl/situation.owl @en
  • dot - Damage Topology Ontology
    https://w3id.org/dot#
    Ontology that defines the topology of damages in constructions. @en
  • dpv - Data Privacy Vocabulary (DPV)
    https://w3id.org/dpv
    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
  • fairo - FAIR-O: An Ontology for FAIR Assessment and Scoring
    https://w3id.org/fair-o
    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
  • sw-quality - SQuAP Ontology
    https://w3id.org/squap/
    Quality, architecture, and process are considered the keystones of software engineering. ISO defines them in three separate standards. However, their interaction has been poorly studied, so far. The SQuAP model (Software Quality, Architecture, Process) describes twenty-eight main factors that impact on software quality in banking systems, and each factor is described as a relation among some characteristics from the three ISO standards. Hence, SQuAP makes such relations emerge rigorously, although informally. SQaAP-Ont is an OWL ontology that formalises those relations in order to represent and reason via Linked Data about software engineering in a three-dimensional model consisting of quality, architecture, and process characteristics. @en