121
results
-
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
-
The DNS Security Ontology (DSecO) project is a data model for representing and reasoning on Domain Name System (DNS) data. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing a DNS Knowledge Graph (KG) for administration and security assessment 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: - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @en
-
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
-
CTRLont specifies concepts and relationships of control actors on a high level @en
-
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
-
Information about authentication providers which might be identity providers or other services such as ones providing JSON Web Tokens. @en
-
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
-
This ontology provides the terms necessary to describe the status of traffic lights. @en
-
The aim of the Occupant Feedback Ontology is to semantically describe passive and active occupant feedback and to enable integration of this feedback with linked building data. @en
-
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
-
An ontology for describing programming language-specific runners, processors and pipelines in RDF-based data processing frameworks. @en
-
## 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
-
An ontology to model accountability of generic systems. @en
-
Smart Building Evacuation Ontology (SBEO) is an ontology that couples the information about any building with its occupants such that it can be used in many useful ways. For example, indoor localization of people, detection of any hazard, a recommendation of normal routes such as shopping or stadium seating routes, or safe and feasible emergency evacuation routes or both of them all together. The core SBEO covers the concepts related to the geometry of building, devices and components of the building, route graphs correspondent to the building topology, users' characteristics and preferences, situational awareness of both building (hazard detection, status of routes in terms of availability and occupancy) and users (tracking, management of groups, status in terms of fitness), and emergency evacuation. @en
-
An ontology to model accountability of AI systems which use machine learning. @en