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  • bdi - Belief-Desire-Intention Ontology
    https://w3id.org/fossr/ontology/bdi/
    The BDI Ontology provides a formal framework to model the Belief-Desire-Intention (BDI) architecture for rational agents. It defines key mental states—Beliefs, Desires, and Intentions—and their relationships, capturing the agent’s reasoning, motivation, and commitment to action. Supporting classes include Propositions (content of mental states), Justifications (rationale for mental states), Plans (action sequences for goals), and TimeIntervals (temporal validity of entities). Key properties like hasBelief, hasDesire, and hasIntention link agents to mental states, while fulfills, adoptsIntention, and motivatesDesire model dynamic interactions. Temporal properties enable reasoning about time-sensitive states and plans. Axioms ensure consistency, such as disjointness between mental states and domain-specific constraints. This ontology supports reasoning, querying, and analysis of agent behaviour, enabling applications in AI, multi-agent systems, and decision support. @en
  • puv - Parameter Usage Vocabulary ontology
    https://w3id.org/env/puv
    A simple ontology which implements the Parameter Usage Vocabulary semantic model, as described at https://github.com/nvs-vocabs/P01 @en
  • eppl - The EP-Plan ontology
    https://w3id.org/ep-plan
    PROV extension for linking Plans and parts of plans to their respective executions. @en
  • chameo - CHAracterisation MEthodology Ontology
    https://w3id.org/emmo/domain/characterisation-methodology/chameo
    CHAMEO is a domain ontology designed to model the common aspects across the different characterisation techniques and methodologies. @en
  • ishi - Ishikawa diagram ontology
    https://w3id.org/ishikawa-diagram-ontology
    The Ishikawa ontology aims to provide a data and view model to manage data encoded in Ishikawa diagrams which are also known as fishbone or cause and effect diagram (CED). Ishikawa diagrams result from (iterative) workshops. Thus, the ontology includes the basic modelling of workshops to create Ishikawa diagrams. @en
  • ids - IDS Information Model
    https://w3id.org/idsa/core
    This ontology defines classes and properties for describing participants, infrastructure, data and services of the International Data Spaces (formerly known as Industrial Data Space). @en
  • authn_provider - Authentication Provider
    https://w3id.org/inrupt/namespace/vocab/authn_provider/
    Information about authentication providers which might be identity providers or other services such as ones providing JSON Web Tokens. @en
  • icon - ICON Ontology
    https://w3id.org/icon/ontology/
    The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. @en
  • loin - Level of Information Need (LOIN) Ontology
    https://w3id.org/loin
    The Level of Information Need (LOIN) Ontology is defined for specifying information requirements for delivery of data in a buildings' life cycle. The LOIN ontology is based on the standard BS EN 17412-1 (2020). Furthermore, it is extended with vocabulary for connecing Information Delivery Specifications (IDS) and Information containers for linked document delivery (ICDD) as per ISO 21597-1 (2020). @en
  • ontouml - OntoUML Metamodel Vocabulary
    https://w3id.org/ontouml
    A reference implementation of the OntoUML metamodel in OWL. @en
  • noria - The NORIA Ontology
    https://w3id.org/noria/ontology/
    The NORIA-O project is a data model for IT networks, events and operations information. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing an IT Service Management (ITSM) Knowledge Graph (KG) for Anomaly Detection (AD) and Risk Management applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g. with `rdfs:subClassOf`, `owl:equivalentClass`). Directions for direct instanciation of these vocabularies are provided for cases where implementing a class/property alignment is redundant. Alignment holds for the following vocabulary releases: - [BBO](https://hal.archives-ouvertes.fr/hal-02365012/) 1.0.0 - [BOT](https://w3id.org/bot/) 0.3.2 - [DevOps-Infra](https://oeg-upm.github.io/devops-infra/) 1.0.0 - [FOLIO](https://github.com/IBCNServices/Folio-Ontology) 1.0.0 - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [PEP](https://w3id.org/pep/) 1.1 - [SEAS](https://w3id.org/seas/) 1.1 - [SLOGERT](https://sepses.ifs.tuwien.ac.at/ns/log/index-en.html) 1.1.0 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @en
  • sdont - The Software Description Ontology
    https://w3id.org/okn/o/sd
    An ontology for describing software and their links to inputs, outputs and variables. The ontology extends schema.org and codemeta vocabularies @en
  • okh - Open Know How (OKH) ontology
    https://w3id.org/oseg/ont/okh
    Used for indexing, searching and comparing Open Source Hardware projects @en
  • nno - The Neural Network Ontology
    https://w3id.org/nno/ontology
    This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @en
  • pep - Process Execution ontology.
    https://w3id.org/pep/
    The process execution ontology is a proposal for a simple extension of both the [W3C Semantic Sensor Network](https://www.w3.org/TR/vocab-ssn/) and the [Semantic Actuator Network](https://www.irit.fr/recherches/MELODI/ontologies/SAN.owl) ontology cores. @en