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The ontology 'dtype' provides a specification of simple data types such as enumerations. These are needed in support of the conversion of XML Schemas and UML Models to OWL. Codelists are also defined in 'dtype'. @en
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The provenance ontology supports data management and auditing tasks. It is used to define the different types of named graphs we used in the store (quad store) and enables their association with metadata that allow us to manage, validate and expose data to BBC services @en
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A Knowledge Model to describe a smart city, that interconnect data from infomobility service, Open Data and other source @en
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An ontology for describing changes between OWL ontology versions @en
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Extension to the Data Privacy Vocabulary (DPV) providing additional categories of personal data @en
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This ontology is an evolution of IRE ontology. It describes identification of resources on the Web, through the definition of relationships between resources and their representations on the Web. The requirement is to describe what can be identified by URIs and how this is handled e.g. in form of HTTP requests and reponds. @en
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The Cochrane Core ontology describes the entities and concepts that exist in the domain of evidence based healthcare. It is used for the construction of the Cochrane Linked Data Vocabulary containing some 400k terms including Interventions (Drugs, Procedures etc), Populations (Age, Sex, Condition), and clinical Outcomes. @en
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The PICO ontology provides a machine accessible version of the PICO framework. It essentially provides a model for describing evidence in a consistent way. The model allows the specifying of complex populations, detailed interventions and their comparisons as well as the outcomes considered. The PICO ontology was originally designed to model the questions asked and answered in Cochrane's systematic reviews. As a leader in the field of evidence based healthcare Cochrane uses the PICO model when framing and publishing evidence based questions. The PICO model is widely adopted for describing healthcare evidence, furthermore is equally applicable in other evidence-based domains. It essentially provides a model for describing evidence in a consistent way. @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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Ontology for public services and organizations @en
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Open 311 Ontology This ontology generalizes the concepts that appear in 311 open data files published by several cities (Toronto, New York, Chicago, Vancouver) across North America. It provides a generis representation of 311 data that other cities can map their data onto and be used as a means of achieving interoperability. @en
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This ontology defines concepts related to federation of internet infrastructures. @en
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The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenance model. (3) To define provenance in a precise, technology-agnostic manner. (4) To support a digital representation of provenance for any 'thing', whether produced by computer systems or not. (5) To allow multiple levels of description to coexist. (6) To define a core set of rules that identify the valid inferences that can be made on provenance representation. @en
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This document specifies a vocabulary for asserting the existence of official endorsements or certifications of agents, such as people and organizations. @en
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Vocabulary for describing ETL and data transformation activities. @en