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  • mls - Machine Learning Schema
    http://www.w3.org/ns/mls
    ML-Schema is a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments @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
  • ontolex - Lexicon Model for Ontologies - Core
    http://www.w3.org/ns/lemon/ontolex
    A model for the representation of lexical information relative to ontologies. Core module. @en
  • synsem - Lexicon Model for Ontologies - Synsem
    http://www.w3.org/ns/lemon/synsem
    A model for the representation of lexical information relative to ontologies. Syntax and semantics module. @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
  • ui - A user interface ontology
    http://www.w3.org/ns/ui
    An ontology suitable for describing forms, sequences in widgets @en
  • decision - Decision ontology
    https://decision-ontology.googlecode.com/svn/trunk/decision.owl
    Decision-making is a process that can result in some decision and decision is a situation of indicating one of the considered options. Decision Ontology provides means for precise distinguishing and distinct treatment of these two aspects. @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
  • aeros - aerOS Continuum Ontology
    https://w3id.org/aerOS/continuum#
    Ontology for the orchestration of the aerOS continuum. @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
  • dg - DINGO Ontology
    https://w3id.org/dingo/
    The DINGO ontology (Data Integration for Grant Ontology) defines the terms of the DINGO vocabulary and provides a machine readable extensible framework to model data relative to projects, funding, project and funding actors, and, notably, funding policies. It is designed to yield high modeling power and elasticity to cope with the huge variety in funding and project practices, which makes it applicable to many areas where funding is an important aspect: first of all research, but also the arts, cultural conservation, and many others. @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
  • aml - AutomationML Ontology
    https://w3id.org/i40/aml
    A vocabulary to represent the AutomationML Standard - IEC 62714 @en
  • iddo - The Interconnected Data Dictionary Ontology (IDDO)
    https://w3id.org/iddo
    The interconnected data dictionary ontology maps the data model of the ISO 23386 for the describing, creating, and maintenance of properties in interconnected data dictionaries. @en