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This vocabulary allows the semantic description of visual analytics applications. It is based on the RDF Data Cube Vocabulary and the Semanticscience Integrated Ontology. @en
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Ontology for the definition of regions and zones of availability on CloudComputing platforms and services. This ontology allows to define model of regions used in large cloud computing providers such as Amazon, Azure, etc. @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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Ontology for Cloud Computing Instances. Instance are classes of VM that comprise varying combinations of CPU, memory, storage, and networking capacity. This ontology allows to define the instantiation model of MVs used in large cloud computing providers such as Amazon, Azure, etc. @en
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The euBusinessGraph (`ebg:`) ontology represents companies, type/status/economic classification, addresses, identifiers, company officers (e.g., directors and CEOs), and dataset offerings. It uses `schema:domainIncludes/rangeIncludes` (which are polymorphic) to describe which properties are applicable to a class, rather than `rdfs:domain/range` (which are monomorphic) to prescribe what classes must be applied to each node using a property. We find that this enables more flexible reuse and combination of different ontologies. We reuse the following ontologies and nomenclatures, and extend them where appropriate with classes and properties: - W3C Org, W3C RegOrg (basic company data), - W3C Time (officer membership), - W3C Locn (addresses), - schema.org (domain/rangeIncludes and various properties) - DBpedia ontology (jurisdiction) - NGEO and Spatial (NUTS administrative divisions) - ADMS (identifiers), - FOAF, SIOC (blog posts), - RAMON, SKOS (NACE economic classifications and various nomenclatures), - VOID (dataset descriptions). This is only a reference. See more detail in the [EBG Semantic Model](https://docs.google.com/document/d/1dhMOTlIOC6dOK_jksJRX0CB-GIRoiYY6fWtCnZArUhU/edit) google document, which includes an informative description of classes and properties, gives examples and data provider rules, and provides more schema and instance diagrams. @en
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The ontology of the taxonomy "European Skills, Competences, qualifications and Occupations". The ontology considers three ESCO pillars (or taxonomy) and 2 registers. The three pillars are: - Occupation - Skill (and competences) - Qualification For the construction and use of the ESCO pillars, the following modelling artefacts are used: - Facetting support to specialize ESCO pillar concepts based on bussiness relevant Concept Groups (e.g. species, languages, ...) - Conept Groups, Thesaurus array and Compound terms (as detailed in ISO 25964) to organize faceted concepts - SKOS mapping properties to relate ESCO pillar concepts to concepts in other (external) taxonomies (e.g. FoET, ISCO88 and ISCO08. More mappings can be added in the future.) - Tagging ESCO pillar concepts by other (external) taxonomies (NUTS, EQF, NACE, ...) - Capture gender specifics on the labels of the ESCO pillar concepts - Rich ESCO concept relationships holding a description and other specific characteristics of the relation between two ESCO pillar concepts. ESCO maintains two additional registers: - Awarding Body - Work Context Awarding Bodies typically are referenced by ESCO qualifications. Occupations can have one or more work context. @en
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A vocabulary to annotate RDF schemas (in particular SHACL shapes) with metadata to define mappings to GraphQL. @en
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The ontology aims at modelling the data on cultural institutes or sites such as data regarding the agents that play a specific role on cultural institutes or sites, the sites themselves, the contact points, all multimedia files which describe the cultural institute or site and any other information useful to the public in order to access the institute or site. Moreover, the ontology represents events that can take place in specific cultural institutes or sites. @en
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Ontology including the content ontology design pattern for modelling objects with states. @en
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This ontology establishes classes corresponding to stereotypes used in ISO-conformant models, as used in the rules for conversion of the ISO TC 211 Harmonized Model from the UML to OWL representations @en
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An OWL representation of the Sampling Features Schema described in clauses 8-10 of ISO 19156:2011 Geographic Information - Observations and Measurements. @en
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An OWL representation of (some of) the basic types described in ISO 19103:2005, required as primitives in other ontologies based on ISO 19100 series standards @en
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A general purpose ontology for observable properties. The ontology supports description of both qualitative and quantitative properties. The allowed scale or units of measure may be specified. A property may be linked to substances-or-taxa and to features or realms, if they play a role in the definition. @en
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Transport Administration Ontology (TAO) for describing data from Swedish Transport Administration Web site. @en
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An ontology for publishing descriptions of historical events as Linked Data, and for mapping between other event-related vocabularies and ontologies. @en