Linked Open Vocabularies

bimerr-op - Occupancy Profile ontology

http://bimerr.iot.linkeddata.es/def/occupancy-profile#

The Occupancy Profile ontology has been developed to represent people’s behavior inside building spaces. @en

brk - Key Register Cadastre (BRK) vocabulary

http://brk.basisregistraties.overheid.nl/def/brk

Vocabulary for the Dutch key register of the cadastre (BRK) @en

brt - Key Register Topography (BRT) vocabulary

http://brt.basisregistraties.overheid.nl/def/top10nl

Vocabulary for the Dutch key register of topography (BRT) @en

plink - PersonLink Ontology

http://cedric.cnam.fr/isid/ontologies/PersonLink.owl

A Multilingual and Multicultural Ontology Representing Family Relationships. @en

va - The Visual Analytics Vocabulary

http://code-research.eu/ontology/visual-analytics

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

agrelon - Agent Relationship Ontology

http://d-nb.info/standards/elementset/agrelon

The ontology of agent relationships, AgRelOn, defines relations of persons to other persons and to organisations @en

ebg - euBusinessGraph ontology

http://data.businessgraph.io/ontology#

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

cochrane - Cochrane Core Vocabulary Ontology

http://data.cochrane.org/ontologies/core/

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

pico - Cochrane PICO Ontology

http://data.cochrane.org/ontologies/pico/

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

esco - The ESCO ontology

http://data.europa.eu/esco/model

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

poste - "La Poste" Ontology

http://data.lirmm.fr/ontologies/poste

This vocabulary describes the contact points of the postal agencies network in France. @en

osp - French Public Services Ontology

http://data.lirmm.fr/ontologies/osp

Ontology for public services and organizations @en

swc - Semantic Web Conference Ontology

http://data.semanticweb.org/ns/swc/ontology

The Semantic Web Conference ontology (SWC) is an ontology for describing academic conferences @en

graphql - GraphQL Vocabulary

http://datashapes.org/graphql

A vocabulary to annotate RDF schemas (in particular SHACL shapes) with metadata to define mappings to GraphQL. @en

ispra - Ispra Ontology

http://dati.isprambiente.it/ontology/core#

ISPRA ontology aims at the description of the processes and activities of the Institute in the areas circumscribed by the first published datasets. @en

Patterns?:	type name both
Flatten?:	yes no

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