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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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This is a vocabulary collection utilized by XHTML Family modules and document types using XHTML Modularization, including XHTML Role and XHTML + RDFa as defined in rdfa-syntax. @en
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Defines the element of Authorization and its essential properties, and also some classes of access such as read and write. @en
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A namespace for describing HTTP messages (http://www.w3.org/Protocols/rfc2616/rfc2616.html) @en
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The Protocol for Web Description Resources (POWDER) allows metadata to be associated with groups of resources such as those found on a Web site. @en
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A set of best practices and simple approach for a read-write Linked Data architecture, based on HTTP access to web resources that describe their state using the RDF data model. @en
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WSMO-Lite is a lightweight approach to the semantic annotation of Web service descriptions, defined by the STI2 working group Conceptual Models for Services @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 eccenca Publish-Subscribe Vocabulary defines concepts and relations to create statements about publishers, subscribers and their subscriptions in a Publish-Subscribe environment based on the PubSubHubbub Core 0.4 specification. @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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To ensure comparability between schemas from different data models, the Description of a Data Source (DSD) vocabulary has been developed. @en
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The DNS Security Ontology (DSecO) project is a data model for representing and reasoning on Domain Name System (DNS) data. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing a DNS Knowledge Graph (KG) for administration and security assessment applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g. with `rdfs:subClassOf`, `owl:equivalentClass`). Directions for direct instanciation of these vocabularies are provided for cases where implementing a class/property alignment is redundant. Alignment holds for the following vocabulary releases: - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @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