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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 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 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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The goal of this ontology design pattern is to characterise a subject or group of subjects of interest by assigning qualifiable or quantifiable attributes or characteristics. @en
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Specification of the metadata used to describe models in the OntoUML/UFO Catalog. @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 Procedural Knowledge Ontology (PKO) addresses the Procedural Knowledge (PK) domain, and models procedures, their executions, and related resources and agents. @en
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The vocabulary allows for the description of data about scientific events such as conferences, symposiums and workshops. @en
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Ontology 'Usability' created to describe and store information about interactions of user with a software user interface @en
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Ontology for representing exceptions to reporting of parents, for entities registered with a Legal Entity Identifier. The Global Legal Identifier System (GLEIS) requires that legal entities with an LEI provide information on their ultimate and direct accounting consolidating parents. Relationship reporting is mandatory with exceptions allowed for certain well-defined reasons. This ontology provides a simple structure for recording reasons for each exception by LEI. @en
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mso-em- MSO-EM: Ontologies for modelling, simulation, optimization (MSO) and epistemic metadata (EM)MSO-EM is a system of ontologies for documenting the knowledge status of models and data; the aim is to make models and data explainable-AI-ready (XAIR). @en
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This ontology was designed to conceptualize symbolic meanings following Baudrillard's Simulacra and Simulation theory. Symbols, their meaning, the context in which the symbolic meaning (or simulation) exists and the source of the simulation are linked to a N-ary Simulation Class. @en