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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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The Data Knowledge Vocabulary allows for a comprehensive description of data assets and enterprise data management. It covers a business data dictionary, data quality management, data governance, the technical infrastructure and many other aspects of enterprise data management. The vocabulary represents a linked data implementation of the Data Knowledge Model which resulted from extensive applied research. @en
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An ontology for describing changes between OWL ontology versions @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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An ontology for aligning existing linguistic ontologies, and for describing the research objects of NLP. @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 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 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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The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. @en
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This ontology is a reduced-in-scope version of the [W3C Decisions and Decision-Making Incubator Group](https://www.w3.org/2005/Incubator/decision/)'s Decision Ontology (DO) which can be found at <https://github.com/nicholascar/decision-o>. It has been re-worked to align entirely with the W3C's [PROV ontology](https://www.w3.org/TR/prov-o/) since it is widely recognised that analysing the elements of decisions *post hoc* is an exercise in provenance. Unlike the original DO, this ontology cannot be used for *normative* scenarios: it is only capable of recording decisions that have already been made (so-called *data-driven* use in the DO). This is because PROV, to which this ontology is completely mapped, does not have a templating system which can indicate what *should* occur in future scenarios. This ontology introduces only one new element for decision modelling over that which was present in the DO: an Agent which allows agency in decision making to be recorded. @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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TAO is a light-weight vocabulary to describe asserted user’s subjective trust values. @en
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The GVP Ontology defines classes, properties and values (skos:Concepts) used in GVP LOD. It is complete regarding AAT and TGN (as of version 2.0), and will be extended in time with more elements needed for the other GVP vocabularies (ULAN, CONA). @en
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This ontology describes terms concerning companies, their cross-border movements within the European Union (EU), and associated EU company legislation. @en
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This RDF vocabulary can be use to describe and categorize annotations involving entity mentions (sub-strings of text) that link to knowledgebase identifiers @en