Linked Open Vocabularies

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Terms
Agents
SPARQL/Dump
Patterns

How to

Select the ontologies from which you want to extract patterns.

Select patterns options:

Name, Type or both:

Name: Ontology elements in the patterns represent specific classes and properties names.

For example:

# INPUT
s4bldg:BuildingSpace rdfs:subClassOf [
  rdf:type owl:Restriction ;
  owl:onProperty s4bldg:isSpaceOf ;
  owl:allValuesFrom [
    rdf:type owl:Class ;
    owl:unionOf (
      s4bldg:Building
      s4bldg:BuildingSpace
    )
  ]
]

# OUTPUT
Pattern 16
owl:Class
|rdfs:subClassOf
| |owl:Restriction
| | |owl:allValuesFrom
| | | |owl:Class
| | | | |owl:unionOf
| | | | | |rdf:List
| | | | | | |rdf:first
| | | | | | | |owl:Class
| | | | | | |rdf:rest
| | | | | | | |rdf:List
| | | | | | | | |rdf:first
| | | | | | | | | |owl:Class
| | | | | | | | |rdf:rest
| | | | | | | | | |rdf:nil
| | |owl:onProperty

# INPUT
s4bldg:BuildingSpace rdfs:subClassOf [
  rdf:type owl:Restriction ;
  owl:onProperty s4bldg:isSpaceOf ;
  owl:allValuesFrom [
    rdf:type owl:Class ;
    owl:unionOf (
      s4bldg:Building
      s4bldg:BuildingSpace
    )
  ]
]

# OUTPUT
Pattern 16
owl:Class
|rdfs:subClassOf
| |owl:Restriction
| | |owl:allValuesFrom
| | | |owl:Class
| | | | |owl:unionOf
| | |owl:onProperty

Type: Ontology elements in the patterns are generalized according to the OWL elements used.

No specific classes or properties are used, but OWL skeletons like:

owl:Class
  |rdfs:subClassOf
  |  |owl:Restriction
  |  |  |owl:onProperty
  |  |  |  |owl:DatatypeProperty, owl:FunctionalProperty
  |  |  |owl:someValuesFrom
  |  |  |  |rdfs:Datatype

Flatten or not:
1. Flatten: If there are collections of classes or individuals in the pattern, the full collection is not included.
  ¿SERGIO esto aplica a los named? O solo a los que se generalizan como Type?
  
  Otra cosa es que estaría bien indicar algunos ejemplos, pensaba que se incluían 3.
```
owl:Class
  |owl:equivalentClass
  |  |owl:Class
  |  |  |owl:oneOf
```

Output: Tengo que ver el output final y definir esto en detalle

The retrieved .zip file contains the following folders and documents:

“Data” folder. This folder contains the files:
- A
- B
- C
“flatten” folder (if selected): This folder contains the files:
- A
- B
- C
“not_flatten” folder (if selected): This folder contains the files:
- A
- B
- C
HTML folder. This folder contains the files:
- A
- B
- C

144

results

How to

lgdo - LinkedGeoData ontology
http://linkedgeodata.org/ontology

LinkedGeoData ontology has been derived from concepts defined by Open Street Map @en
lsq - The Linked SPARQL Queries Vocabulary (LSQ(V))
http://lsq.aksw.org/vocab

The Linked SPARQL Queries Vocabulary (LSQ(V)), defined using RDF(S) and OWL, provides a machine readable vocabulary to help describe queries in SPARQL logs and their statistics. The vocabulary builds upon the SPIN vocabulary and the Service Description vocabulary. @en
teach - Teaching Core Vocabulary Specification
http://linkedscience.org/teach/ns#

TEACH, the Teaching Core Vocabulary, is a lightweight vocabulary providing terms to enable teachers to relate things in their courses together. The Teaching Core Vocabulary is based on practical requirements set by providing seminar and course descriptions as Linked Data. @en
scip - Scientific People Ontology
http://lod.taxonconcept.org/ontology/sci_people.owl

A vocabulary for description of scientific people, focused on bio sciences @en
nen3610 - NEN 3610 - base model geo-information
http://modellen.geostandaarden.nl/def/nen3610

NEN 3610 - base model geo-information – terms, definitions, relationships and general rules for the exchange of information about earth related spatial objects @en
dcndl - NDL Metadata Terms
http://ndl.go.jp/dcndl/terms/

RDF Schema declaration for Japan NDL Metadata Terms @en
edupro - EduProgression Ontology
http://ns.inria.fr/semed/eduprogression/

The EduProgression ontology formalizes the educational progressions of the French educational system, making possible to represent the existing progressions in a standard formal model, searchable and understandable by machines (OWL). @en
lsweb - ELSEWeb Data Ontology
http://ontology.cybershare.utep.edu/ELSEWeb/elseweb-data.owl

The ELSEWeb Data ontology provides simple classes for describing datasets. This project provides foundational support for the ELSEWeb project(http://elseweb.cybershare.utep.edu/). website: http://elseweb.cybershare.utep.edu/ontologies email: nvillanuevarosales@utep.edu @en
munc - Meta-Uncertainty
http://ns.inria.fr/munc/

An OWL vocabulary for describing uncertainty metadata. @en
dm2e - DM2E model
http://onto.dm2e.eu/schemas/dm2e

The DM2E model is a specialisation of the Europeana Data Model (EDM) and can be used for the description of manuscripts in the cultural heritage domain. @en
limoo - License Model Ontology
http://purl.org/LiMo/0.1#

A vocabulary to describe licenses @en
frapo - Funding, Research Administration and Projects Ontology
http://purl.org/cerif/frapo/

FRAPO, the Funding, Research Administration and Projects Ontology, is a CERIF-compliant ontology written in OWL 2 DL for describing research project administrative information. @en
loc - Location Vocabulary
http://purl.org/ctic/infraestructuras/localizacion

This vocabulary is used to describe the physical location of public places. @en
dce - Dublin Core Metadata Element Set
http://purl.org/dc/elements/1.1/

The Dublin Core Metadata Element Set is a vocabulary of fifteen properties for use in resource description. The name "Dublin" is due to its origin at a 1995 invitational workshop in Dublin, Ohio; "core" because its elements are broad and generic, usable for describing a wide range of resources. @en
dcterms - DCMI Metadata Terms
http://purl.org/dc/terms/

an up-to-date specification of all metadata terms maintained by the Dublin Core Metadata Initiative, including properties, vocabulary encoding schemes, syntax encoding schemes, and classes. @en

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Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (144)
property/class >
agent >

Language

English (110)
French (17)
German (7)
Spanish (7)
Chinese (5)
Dutch (5)
Italian (5)
Japanese (4)
Korean (4)
Portuguese (4)
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Este trabajo ha recibido financiación del proyecto PIONERA (Potenciando la interoperabilidad en espacios de datos mediante inteligencia artificial), un proyecto financiado en el contexto de la convocatoria Productos y Servicios tecnológicos para Espacio de Datos del Ministerio para la Transformación Digital y de la Función Pública en el marco del PRTR financiado por Unión Europea (NextGenerationEU)