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The Open NEE Model defines an extension of the Open Annotation Data Model (http://www.openannotation.org/spec/core) that allows describing in RDF the result of a Named Entity Extraction (NEE) process, enabling thereby an application to run advanced (SPARQL) queries over the annotated data. The model also exploits the Open NEE Configuration Model (http://www.ics.forth.gr/isl/oncm) for relating the output of a NEE process with an applied configuration (serving provenance information to the output of the entire NEE process). @en
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This document is a vocabulary to describe compound measures, i.e. measures with several metric or item that are organized with serveral dimensions. The description of such a measure relies on a Tree-Structure of Requirement (TSoR): a set of requirements structured hierarchicaly with analysis element. A TSoR represents the main measure. Several information may be added to explicitely indicate how the overall score on the measure should be calculated based on the hierarchy, relative importance of the node of the hierarchy and an aggregation function. The measure can be described completely and unambiguously from the organisation to the requirements and the implementation. @en
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An ontology that represents concepts in the legal domain. @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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A set of annotation properties to be used for ontology design patterns @en
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A metadata vocabulary for describing comic books and comic book collections. @en
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The Creative Commons Rights Expression Language (CC REL) lets you describe copyright licenses in RDF @en
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The DNB RDF Vocabulary (dnb:) is a collection of classes, properties and datatypes used within the DNB's linked data service.It complements the GND Ontology (gndo:) which is specifically geared towards authority data from the Integrated Authority File (GND), whereas this vocabulary is more general-purpose. @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 EUropean Research Information Ontology (EURIO) conceptualises, formally encodes and makes available in an open, structured and machine-readable format data about resarch projects funded by the EU's framework programmes for research and innovation. @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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orca, the Ontology of Reasoning, Certainty and Attribution, is an ontology for characterizing the certainty of information, how it is known, and its source @en