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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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APCO is an ontology that allows the description of public procurement terms @en
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The Smart Appliances REFerence (SAREF) ontology is a shared model of consensus that facilitates the matching of existing assets (standards/protocols/datamodels/etc.) in the smart appliances domain. The SAREF ontology provides building blocks that allow separation and recombination of different parts of the ontology depending on specific needs. @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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This is the extension of SAREF for the EEBus and Energy@Home project. The documentation of SAREF4EE is available at http://ontology.tno.nl/SAREF4EE_Documentation_v0.1.pdf. SAREF4EE represents 1) The configuration information exchanged in the use case 'Remote Network Management' according to the EEBus Technical Report, Protocol Specification- Remote Network Management, version 1.0.0.2, 2015-09-19; 2) The scheduling information about power sequences exchanged in the use cases Appliance scheduling through CEM and remote start' and 'Automatic cycle rescheduling', according to the message structures described in General Message Structures, version 0.1.1, 2015-10-07; 3) The monitor and control information exchanged in the use case 'Communicate appliance status and info on manually planned cycles', according to the monitoring and control part of the Energy@Home Data Model, version 1.0; and 4) the event-based data exchanged in the use case 'Demand Response', according to General Message Structures, version 0.1.1, 2015-10-07. @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 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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Ontology for the orchestration of the aerOS continuum. @en
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Simple ontology for Cloud Computing Services. This ontology allows to define model of prices used in large cloud computing providers such as Google, Amazon, Azure, etc., including options for regions, type of instances, prices specification, etc. @en
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The scope of the DIO is the domain of design intent or design rationale that needs to be documented while undertaking the design of any artifact @en
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The DINGO ontology (Data Integration for Grant Ontology) defines the terms of the DINGO vocabulary and provides a machine readable extensible framework to model data relative to projects, funding, project and funding actors, and, notably, funding policies. It is designed to yield high modeling power and elasticity to cope with the huge variety in funding and project practices, which makes it applicable to many areas where funding is an important aspect: first of all research, but also the arts, cultural conservation, and many others. @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