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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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The notion of territory plays a major role in human and social sciences. In an historical context, most approaches are irrelevant as they rely on geometric data, which is not available. In order to represent historical territories,we conceived the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. This approach relies on a notion of building blocks to replace polygonal geometry @en
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The Cultural Event module models cultural events, i.e. events involving cultural properties. @en
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The module Location models information related to the localization and georeferencing of a cultural property. 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/place.owl - http://www.ontologydesignpatterns.org/cp/owl/timeindexedsituation.owl - http://www.ontologydesignpatterns.org/cp/owl/situation.owl @en
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Ontology that defines the topology of damages in constructions. @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