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The SeaLiT Ontology is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous information related to maritime history. It aims at providing the semantic definitions needed to transform disparate, localised information sources of maritime history into a coherent global resource. It also serves as a common language for domain experts and IT developers to formulate requirements and to agree on system functionalities with respect to the correct handling of historical information. The ontology uses and extends the CIDOC Conceptual Reference Model (ISO 21127:2014), in particular version 7.1.1, as a general ontology of human activity, things and events happening in space and time. @en
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Electric Mobility module of the suite of ontologies for urban IoT devices. @en
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Sharing Mobility module of the suite of ontologies for urban IoT devices. @en
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Defines specific air traffic management concepts used in aircraft navigation through the US National Airspace System @en
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Defines concepts related to the structure of the US National Airspace System (NAS) @en
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Defines aircraft models, aircraft systems / subsystems, and aircraft characteristics @en
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Defines concepts related to airport status, including weather, forecasts, and airport operations @en
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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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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 Heat Pump Ontology (HPOnt) aims to formalize and represent all the relevant information of Heat Pumps. The HPOnt has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. @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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This ontology defines feature of interest and their properties, as an extension of the core classes of the SSN ontology (https://www.w3.org/ns/ssn/). A feature of interest is an abstraction of a real world phenomena (thing, person, event, etc). A feature of interest is then defined in terms of its properties, which are qualifiable, quantifiable, observable or operable qualities of the feature of interest. Alignments to other ontologies are proposed in external documents: - [SSNAlignment](https://w3id.org/seas/SSNAlignment) proposes an alignment to the [SSN ontology](http://www.w3.org/ns/ssn/). - [QUDTAlignment](https://w3id.org/seas/QUDTAlignment) proposes an alignment to the [QUDT ontology](http://qudt.org/). @en
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The Rail Topology Ontology models foundational rail topology concepts.. @en