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The Registered Organization Vocabulary is a profile of the Organization Ontology for describing organizations that have gained legal entity status through a formal registration process, typically in a national or regional register. @en
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The ontology is aimed at the support of research groups in the field of Business Modeling and Knowledge Engineering (BMaKE) in their collaborative work for qualitatively analyzing scholarly papers as well as sharing the results of that analyses and judgements. @en
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This ontology is intended to describe Semantic Actuator Networks, as a counterpoint to SSN definition of Semantic Sensor Networks. An actuator is a physical device having an effect on the world (see Actuator for more information). It is worth noticing that some concepts are imported from SSN, but not SSN as a whole. This is a design choice intended to separate as much as possible the definition on actuator from the definition of sensor, which are completely different concept that can be used independantly from each other. This ontology is used as a ontological module in IoT-O ontology. @en
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Service Level Agreement for Cloud Computing Services. This ontology allows to define model of SLA/SLO used in large cloud computing providers such as Amazon, Azure, etc., including terms, claims, credit, compensations, etc @en
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This ontology is a composition of some content design patterns for the semiotic triangle. Its structure is extracted from DOLCE-Ultralite (DOLCE+c.DnS), but it uses a different terminology, @en
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This vocabulary is a component of Ludo. It was created to describe and represent the graphical elements of a serious game. It it based on "Game Content Model: An Ontology for Documenting Serious Game Design" by Tang, S et al. @en
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DBpedia Data ID is an ontology with the goal of describing LOD datasets via RDF files in a uniform way. Established vocabularies like DCAT, VoID, Prov-O and SPARQL Service Description are used for maximum compatibility. @en
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The AMLO-core is the main module of the AMLO projects that extends the Financial Industry Business Ontology (FIBO) with some concepts to describe the Anti Money Laundering (AML) knowledge and facts. @en
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The generic BBC ontology for people, places,events, organisations, themes which represent things that make sense across the BBC. This model is meant to be generic enough, and allow clients (domain experts) link their own concepts @en
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The objective of SAREF4EHAW is to extend SAREF ontology for the eHealth/Ageing-well (EHAW) vertical. Clause 4.1 of the present document shortly introduces a high level view of the envisioned SAREF4EHAW semantic model and modular ontology, with the retained concepts (i.e. classes) and their relations. SAREF4EHAW extension has been specified and formalised by investigating EHAW domain related resources, as reported in ETSI TR 103 509, such as: potential stakeholders, standardization initiatives, alliances/associations, European projects, EC directives, existing ontologies, and data repositories. Therefore, SAREF4EHAW modular ontology shall both: - Allow the implementation of a limited set of typical EHAW related use cases already identified in ETSI TR 103 509, i.e. - Use case 1 ?elderly at home monitoring and support?, - Use case 2 ?monitoring and support of healthy lifestyles for citizens?, - Use case 3 ?Early Warning System (EWS) and Cardiovascular Accidents detection?. - Fulfil the eHealth Ageing Well related requirements provided in ETSI TR 103 509, mainly the ontological ones that were mostly taken as input for the ontology specification. SAREF4EHAW mainly reuses the following existing ontologies: SAREF, ETSI SmartBAN reference model, SAREF 4 Environment extension and W3C SSN System module. The following figure presents the high level view of SAREF4EHAW ontology.  For semantic interoperability handling purposes, an ontology based solution, combined with sensing-as-a-service and WoT strategies, is retained for SAREF4EHAW. Therefore, an upper level ontology, at service level, shall also behas been fully modelled (Service class and sub-classes depicted in the previous figure). For embedded semantic analytics purposes, SAREF4EHAW shall behas been designed using the modularity principle (see ETSI TR 103 509) and can thus be mainly described by the following self-contained knowledge sub-ontologies (or modules): HealthActor, Ban, HealthDevice, Function (measured data related concepts included) and Service. @en
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Lemon: The lexicon model for ontologies is designed to allow for descriptions of lexical information regarding ontological elements and other RDF resources. Lemon covers mapping of lexical decomposition, phrase structure, syntax, variation, morphology, and lexicon-ontology mapping. @en
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The participation ontology is a simple model for describing the roles that people play within groups. It is intended that specific domains will create subclasses of roles within their own areas of expertise. @en
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This ontology contains geographic feature classes and associated properties including classes and properties for describing the spatial location of the geographic feature. The classes and properties have been defined based on an ESRI dataset. @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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This is an ontology representation of the climatic data variables defined by Climate and Forecast (CF) standard names vocabulary (http://cf-pcmdi.llnl.gov/documents/cf-standard-names/), maintained by the Program for Climate Model Diagnosis and Intercomparison (http://cf-pcmdi.llnl.gov/ ) which is intended for use with climate and forecast data, in the atmosphere, surface and ocean domains. @en