108
results
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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 -
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 -
ddesc - Denotative Description Ontology (ArCo network) https://w3id.org/arco/ontology/denotative-description The Denotative Description module encodes the characteristics of a cultural property, as detectable and/or detected during the cataloguing process and measurable according to a reference system. Examples include measurements e.g. length, constituting materials e.g. clay, employed techniques e.g. melting, conservation status e.g. good, decent, bad. In this module are used as template the following Ontology Design Patterns: - http://www.ontologydesignpatterns.org/cp/owl/collectionentity.owl - http://www.ontologydesignpatterns.org/cp/owl/classification.owl - http://www.ontologydesignpatterns.org/cp/owl/descriptionandsituation.owl - http://www.ontologydesignpatterns.org/cp/owl/situation.owl @en -
arco - Core Ontology (ArCo network) https://w3id.org/arco/ontology/core The Core module represents general-purpose concepts orthogonal to the whole network, which are imported by all other ontology modules (e.g. part-whole relation, classification). @en -
cdesc - Context Description Ontology (ArCo network) https://w3id.org/arco/ontology/context-description The Context Description module includes models for the context of a cultural property, in a broad sense: agents (e.g.: author, collector, copyright holder), objects (e.g.: inventories, bibliography, protective measures, other cultural properties, collections etc.), activities (e.g.: surveys, conservation interventions), situations (e.g.: commission, coin issuance, estimate, legal situation) related, involved or involving the cultural property. Thus it represents attributes that do not result from a measurement of features in a cultural property, but are associated with it. @en -
pd - Personal Data Categories https://w3id.org/dpv/pd Extension to the Data Privacy Vocabulary (DPV) providing additional categories of personal data @en -
seasbo - The SEAS Building Ontology https://w3id.org/seas/BuildingOntology The SEAS Building ontology describes a taxonomy of buildings, building spaces, and rooms. Some categorizations are based on the energy efficiency related to their insulation etc., although the actual values for classes depend the country specific regulations and geographical locations. Other categorizations are based on occupancy and activities. There is no single accepted categorization available. This taxonomy uses some types selected from: - International building occupancy based categories (USA) - The Classification of Types of Constructions (EU) - Finnish building categorization VTJ2000 (Finland) - Wikipedia category page for Rooms: https://en.wikipedia.org/wiki/Category:Rooms @en -
modsci - ModSci, Modern Science Ontology. https://w3id.org/skgo/modsci# ModSci is a reference ontology for modelling different types of modern sciences and related entities, such as scientific discoveries, renowned scientists, instruments, phenomena ... etc. @en -
sw-quality - SQuAP Ontology https://w3id.org/squap/ 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 -
sri - Smart Readiness Indicator Vocabulary https://w3id.org/sri A vocabulary specifying concepts and structures needed to represent different data cubes needed for the Smart Readiness Indicator. @en -
vair - Vocabulary of AI Risks https://w3id.org/vair VAIR is a taxonomy of AI and risk concepts. @en -
rml-core http://w3id.org/rml/core -
rml-fnml http://w3id.org/rml/fnml/ -
arco https://w3id.org/arco/ontology/core -
rml-io http://w3id.org/rml/io/