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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
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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
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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
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Information about authentication providers which might be identity providers or other services such as ones providing JSON Web Tokens. @en
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A reference implementation of the OntoUML metamodel in OWL. @en
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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
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## RDF Presentation and RDF Presentation Negotiation An RDF graph can be presented in several ways, using different media types. Examples of RDF media types include `application/rdf+xml`, `text/turtle`, `application/json+ld`. Today, most of the content consumed/produced/published, on the Web is not presented in RDF. In the Web of Things, HTTP servers and clients would rather exchange lightweight documents, potentially binary. Currently, most existing RDF Presentations generically apply to any RDF graph, at the cost of being heavy text-based documents. Yet, lightweight HTTP servers/clients could be better satisfied with consuming/producing/publishing lightweight documents, may its structure be application-specific. @en
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An ontology to model accountability of generic systems. @en
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An ontology to model accountability of AI systems which use machine learning. @en
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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
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A vocabulary specifying concepts and structures needed to represent different data cubes needed for the Smart Readiness Indicator. @en
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RDF-STaX is an OWL 2 DL ontology that enables describing the types of RDF streams and defines relations between them. @en
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Ontology that defines core concepts for representing YANG servers, including connection details and the available YANG datastores, along with operations for retrieving YANG data from a YANG server. The goal of this ontology is to enable the declarative and abstract of the interactions with YANG servers for monitoring and configuration purposes. In this sense, the ontology can become the basis for building a knowledge graph from YANG data obtained from YANG servers. @en
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This ontology is for use in describing Workspaces. Workspaces are places where data is stored and associated policies of privacy. A given application typically stores information in several different workspaces, some being user private, some shared, and some public. it is crucial that the user has easy control over the destiny of information. @en
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Module for data schema specifications, part of the W3C Web of Things (WoT) Thing Description model @en