Linked Open Vocabularies

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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

919

results

How to

ends - Vocabulary of endpoint status (availability, responseTime)
http://labs.mondeca.com/vocab/endpointStatus

Endpoint Status vocabulary intends to describe endpoint availability @en
lawd - Linking Ancient World Data Ontology
http://lawd.info/ontology/

The goal of LAWD is to fill in the cracks between the data used and published by projects with a focus on the ancient world and the standard Linked Data vocablary schemes, like Dublin Core, the Open Annotation Collaboration, and CIDOC-CRM. @en
dso - Data Structure Ontology
http://inference-web.org/2.0/ds.owl

This ontology offers OWL-Lite definition for object list. It is a restricted version of OWL-S ObjectList @en
pmlp - PML2 provenance ontology
http://inference-web.org/2.0/pml-provenance.owl

The provenance part of PML2 ontology. It is a fundamental component of PML2 ontology. @en
isbd - ISBD elements
http://iflastandards.info/ns/isbd/elements/

This is a registration of classes and properties from International Standard Bibliographic Description (ISBD), consolidated edition, published by De Gruyter Saur in July 2011 (ISBN 978-3-11-026379-4). @en
frbrer - FRBRer model
http://iflastandards.info/ns/fr/frbr/frbrer/

This is the element set of native RDF classes and properties described in the current text (Feb 2009) of the Functional Requirements for Bibliographic Records (FRBR) entity-relationship model. @en
hr - hRESTS Ontology
http://iserve.kmi.open.ac.uk/ns/hrests

hRESTS is a vocabulary for describing RESTful Web services @en
msm - Minimal Service Model
http://iserve.kmi.open.ac.uk/ns/msm

A simple RDF(S) ontology able to capture (part of) the semantics of both Web services and Web APIs @en
lemon - LExicon Model for ONtologies
http://lemon-model.net/lemon

Lemon: The lexicon model for ontologies is designed to allow for descriptions of lexical information regarding ontological elements and other RDF resources. Lemon covers mapping of lexical decomposition, phrase structure, syntax, variation, morphology, and lexicon-ontology mapping. @en
spfood - SmartProducts Food Domain Model
http://kmi.open.ac.uk/projects/smartproducts/ontologies/food.owl

Defines concepts and properties specific for the SmartProducts Philips use case (cooking domain). @en
moat - Meaning of a Tag Ontology
http://moat-project.org/ns#

An ontology that let users define relationships between Tag objects and URIs of Semantic Web resources @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
mexcore - MEX Core Vocabulary
http://mex.aksw.org/mex-core

MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-core layer represents the core information gathered from a basic machine learning experiment design. @en
mexalgo - MEX Algorithm Ontology
http://mex.aksw.org/mex-algo

MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-algo layer represents the algorithm information existing into a basic machine learning experiment. @en
mexperf - MEX Performance Ontology
http://mex.aksw.org/mex-perf

MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-perf layer is the 3rd level of the MEX for representing the machine learning algorithm's performances. @en

1...14 151617 18...62

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (919)
property/class >
agent >

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English (706)
French (78)
Spanish (59)
Italian (50)
German (48)
Dutch (23)
Portuguese (23)
Russian (19)
Japanese (16)
Chinese (11)
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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)