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

206

results

How to

ekc - EKC Data Model Vocabulary: Ontology Design for the Encyclopedic Archives of Korean Culture (ekc)
http://dh.aks.ac.kr/ontologies/ekc

The present specification is based on the document \"Ontology:EKC 2022\", originally led by Hyeon Kim in the Center for Digital Humanities at the Academy of Korean Studies. @en
obws - Object with states ontology
http://delicias.dia.fi.upm.es/ontologies/ObjectWithStates.owl

Ontology including the content ontology design pattern for modelling objects with states. @en
h2o - Ontology for conversion of ISO/TC 211
http://def.seegrid.csiro.au/isotc211/iso19150/-2/2012/basic

This ontology establishes classes corresponding to stereotypes used in ISO-conformant models, as used in the rules for conversion of the ISO TC 211 Harmonized Model from the UML to OWL representations @en
om - ISO 19156 Observation Model
http://def.seegrid.csiro.au/isotc211/iso19156/2011/observation

An OWL representation of the Observation Schema described in clause 6 of ISO 19156:2011 Geographic Information - Observations and Measurements @en
oml - OWL for Observations
http://def.seegrid.csiro.au/ontology/om/om-lite

An OWL representation of the Observation Schema described in clauses 7-8 of ISO 19156:2011 Geographic Information - Observations and Measurements / OGC Abstract Specification - Topic 20. @en
samfl - OWL for Sampling Features
http://def.seegrid.csiro.au/ontology/om/sam-lite

A simple OWL representation of the Sampling Features Schema described in clauses 9-11 of ISO 19156:2011 Geographic Information - Observations and Measurements @en
gf - OWL representation of ISO 19109 (General Feature Model)
http://def.seegrid.csiro.au/isotc211/iso19109/2005/feature

An OWL representation of part of the General Feature Model described in ISO 19109 and the General Feature Instance model described in Annex C of ISO 19156:2011. @en
sam - The Sampling Features Schema Vocabulary
http://def.seegrid.csiro.au/isotc211/iso19156/2011/sampling

An OWL representation of the Sampling Features Schema described in clauses 8-10 of ISO 19156:2011 Geographic Information - Observations and Measurements. @en
md - OWL representation of ISO 19115
http://def.seegrid.csiro.au/isotc211/iso19115/2003/metadata

An OWL representation of parts of the Geographic Metadata model described in ISO 19115:2003 with Corrigendum 2006 - MD Package. @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
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
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

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Flatten?:	yes no

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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)