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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
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This document describes functions which transform HTTP representations, i.e., the actual literal payloads of HTTP messages. @en
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An Ontology for representing EDIFACT Messages. @en
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The eccenca Publish-Subscribe Vocabulary defines concepts and relations to create statements about publishers, subscribers and their subscriptions in a Publish-Subscribe environment based on the PubSubHubbub Core 0.4 specification. @en
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The Crime Event Model is an ontology for the representation of crime events extracted from local newspapers. It could be employed for Crime Analysis purposes: extracting crime information from newspapers and enriching them with proper machine-readable semantics is a critical task to help law enforcement agencies at preventing crime, supporting criminal investigations and evaluating the action of law enforcement agencies themselves. The model is based on the fundamental 5W1H journalistic questions, that are Who?, What?, When?, Where?, Why? and How?. Another important requirement was the attempt to exploit existing knowledge graphs and ontologies such as the Simple Event Model (SEM) Ontology and the Schema.org data model for interoperability and interconnection. @en
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Ontology for the orchestration of the aerOS continuum. @en
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This document is a vocabulary to describe compound measures, i.e. measures with several metric or item that are organized with serveral dimensions. The description of such a measure relies on a Tree-Structure of Requirement (TSoR): a set of requirements structured hierarchicaly with analysis element. A TSoR represents the main measure. Several information may be added to explicitely indicate how the overall score on the measure should be calculated based on the hierarchy, relative importance of the node of the hierarchy and an aggregation function. The measure can be described completely and unambiguously from the organisation to the requirements and the implementation. @en
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CiteDCAT-AP is an extension of the DCAT application profile for data portals in Europe (DCAT-AP) for describing resources documented by using the DataCite metadata schema - the de facto standard for data citation, and used across scientific disciplines. Its basic use case is to make research data searchable on general data portals, thereby bridging the gap between scientific and public sector information. For this purpose, CiteDCAT-AP provides an RDF vocabulary and the corresponding RDF syntax binding for the metadata elements defined in DataCite. @en
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The scope of the DIO is the domain of design intent or design rationale that needs to be documented while undertaking the design of any artifact @en
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To ensure comparability between schemas from different data models, the Description of a Data Source (DSD) vocabulary has been developed. @en
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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
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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
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An ontology to describe experiments, evaluations and their relation. @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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A simple ontology which implements the Parameter Usage Vocabulary semantic model, as described at https://github.com/nvs-vocabs/P01 @en