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This ontology models personalized tourist experiences by representing cities, points of interest, events, accommodations, restaurants, transportation, and their relationships. This ontology is part of a university project. @en
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This is the extension of SAREF for the EEBus and Energy@Home project. The documentation of SAREF4EE is available at http://ontology.tno.nl/SAREF4EE_Documentation_v0.1.pdf. SAREF4EE represents 1) The configuration information exchanged in the use case 'Remote Network Management' according to the EEBus Technical Report, Protocol Specification- Remote Network Management, version 1.0.0.2, 2015-09-19; 2) The scheduling information about power sequences exchanged in the use cases Appliance scheduling through CEM and remote start' and 'Automatic cycle rescheduling', according to the message structures described in General Message Structures, version 0.1.1, 2015-10-07; 3) The monitor and control information exchanged in the use case 'Communicate appliance status and info on manually planned cycles', according to the monitoring and control part of the Energy@Home Data Model, version 1.0; and 4) the event-based data exchanged in the use case 'Demand Response', according to General Message Structures, version 0.1.1, 2015-10-07. @en
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This ontology extends the SAREF ontology for the environment domain, specifically for the light pollution domain, including concepts like photometers, light, etc. @en
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The development of the SAREF4GRID ontology has been partially funded by the IA4TES project (MIA.2021.M04.0008), funded by the Spanish Ministry of Economic Affairs and Digital Transformation and by the NextGenerationEU program @en
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This ontology extends the SAREF ontology for the water domain. This work has been developed in the context of the STF 566, which was established with the goal to create three SAREF extensions, one of them for the water domain. @en
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A vocabulary & data model for describing RDF changes and revisions. It defines the Commit & Revision classes together with their expected properties. @en
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GConsent provides concepts and relationships for defining consent and its associated information or metadata with a view towards GDPR compliance. It is the outcome of an analysis of consent and requirements associated with obtaining, using, and changes in consent as per the GDPR. The ontology also provides an approach to using its terms in various scenarios and use-cases (see more information in the documentation) which is intended to assist in its adoption. @en
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GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en
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The General Data Protection Regulation (GDPR) is comprised of several articles, each with points that refer to specific concepts. The general convention of referring to these points and concepts is to quote the specific article or point using a human-readable reference. This ontology provides a way to refer to the points within the GDPR using the EurLex ontology published by the European Publication Office. It also defines the concepts defined, mentioned, and requried by the GDPR using the Simple Knowledge Organization System (SKOS) ontology. @en
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AIRO represents AI risk concepts and relations based on the AI Act draft and ISO 31000 standard series. @en
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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
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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
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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
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The ontology 'dtype' provides a specification of simple data types such as enumerations. These are needed in support of the conversion of XML Schemas and UML Models to OWL. Codelists are also defined in 'dtype'. @en
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MOAC, the Management of a Crisis Vocabulary, is a lightweight vocabulary aiming to provide terms to enable practitioners to relate different "things" in crisis management activities together as Linked Data. The initial MOAC terms originated from the Inter Agency Standing Committee (IASC), Emergency Shelter Cluster in Haiti, UNOCHA 3W Who What Where Contact Database @en