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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 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 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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Extension to the Data Privacy Vocabulary (DPV) providing additional categories of personal data @en
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This ontology describes wildlife observations generated by sensors. @en
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This ontology defines a vocabulary for describing carbon emission conversion factors (CF). These are values typically used to calculate carbon emissions where the CF multiplies a quantified estimate of the energy (e.g., kWh of electricity, litters of fuel, etc.) used by a particular activity. @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
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The I-ADOPT Framework ontology @en
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OWL ontology for the IFC conceptual data schema and exchange file format for Building Information Model (BIM) data @en
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The Ontology for Property Management (OPM) extends the concepts introduced in the Smart Energy Aware Systems (SEAS) Evaluations ontology. @en
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This ontology describes the components, failures, sensors, and events related to offshore wind platforms. @en
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This ontology defines a vocabulary for describing provenance traces of carbon emission calculations by capturing the quantifiable measurements of carbon emission sources used by some activities (e.g., electricity used by a machinery to produce a product, petrol used to make a car journey, etc.) and emission conversion factors used to estimate the carbon emissions produced by these. In addition, the ontology provides the ability to capture various data transformations that occurred before energy estimates may be used with relevant conversion factors. For example, sensors may provide raw readings about a water flow of an irrigation rig in an agri-food operation which is then used as a proxy to estimate the total volume of fertilisers used. @en
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This ontology defines: - a set of subclasses of `seas:Evaluation` to better interpret evaluations of quantifiable properties. - a set of sub properties of `seas:hasProperty` to qualify time-related properties. @en
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The SEAS Device ontology defines `seas:Device` as physical system that are designed to execute one or more procedures that involve the physical world. @en