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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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This ontology is intended to describe Semantic Actuator Networks, as a counterpoint to SSN definition of Semantic Sensor Networks. An actuator is a physical device having an effect on the world (see Actuator for more information). It is worth noticing that some concepts are imported from SSN, but not SSN as a whole. This is a design choice intended to separate as much as possible the definition on actuator from the definition of sensor, which are completely different concept that can be used independantly from each other. This ontology is used as a ontological module in IoT-O ontology. @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
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The Ontology of units of Measure (OM) 2.0 models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. @en
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A Knowledge Model to describe a smart city, that interconnect data from infomobility service, Open Data and other source @en
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domOS Common Ontology (dCO) represents a common information model to share a unified understanding for humans and machines and to ensure semantic interoperability in a heterogeneous IoT infrastructure. This ontology allows the decoupling of the infrastructure from the software services and applications. @en
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This ontology describes wildlife observations generated by sensors. @en
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Extension to the Data Privacy Vocabulary (DPV) providing additional categories of personal data @en
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SAREF4INMA is an extension of SAREF for the industry and manufacturing domain. SAREF4INMA focuses on extending SAREF for the industry and manufacturing domain to solve the lack of interoperability between various types of production equipment that produce items in a factory and, once outside the factory, between different organizations in the value chain to uniquely track back the produced items to the corresponding production equipment, batches, material and precise time in which they were manufactured. SAREF4INMA is specified and published by ETSI in the TS 103 410-5 associated to this ontology file. SAREF4INMA was created to be aligned with related initiatives in the smart industry and manufacturing domain in terms of modelling and standardization, such as the Reference Architecture Model for Industry 4.0 (RAMI), which combines several standards used by the various national initiatives in Europe that support digitalization in manufacturing. The full list of use cases, standards and requirements that guided the creation of SAREF4INMA are described in the associated ETSI TR 103 507. @en
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IoT-O is a core domain Internet of Things ontology. It is intended to model horizontal knowledge about IoT systems and applications, and to be extended with vertical, application specific knowledge. It is constituted of different modules : - A sensing module, based on W3C's SSN (http://purl.oclc.org/NET/ssnx/ssn) - An acting module, based on SAN (http://www.irit.fr/recherches/MELODI/ontologies/SAN) - A service module, based on MSM (http://iserve.kmi.open.ac.uk/ns/msm/msm-2014-09-03.rdf) and hRest (http://www.wsmo.org/ns/hrests) - A lifecycle module, based on a lifecycle vocabulary (http://vocab.org/lifecycle/schema-20080603.rdf) and an iot-specific extension (http://www.irit.fr/recherches/MELODI/ontologies/IoT-Lifecycle) - An energy module, based on powerOnt (ttp://elite.polito.it/ontologies/poweront.owl) IoT-O developping team also contributes to the oneM2M IoT interoperability standard. @en
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The Cochrane Core ontology describes the entities and concepts that exist in the domain of evidence based healthcare. It is used for the construction of the Cochrane Linked Data Vocabulary containing some 400k terms including Interventions (Drugs, Procedures etc), Populations (Age, Sex, Condition), and clinical Outcomes. @en
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The PICO ontology provides a machine accessible version of the PICO framework. It essentially provides a model for describing evidence in a consistent way. The model allows the specifying of complex populations, detailed interventions and their comparisons as well as the outcomes considered. The PICO ontology was originally designed to model the questions asked and answered in Cochrane's systematic reviews. As a leader in the field of evidence based healthcare Cochrane uses the PICO model when framing and publishing evidence based questions. The PICO model is widely adopted for describing healthcare evidence, furthermore is equally applicable in other evidence-based domains. It essentially provides a model for describing evidence in a consistent way. @en