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

108

results

How to

juso - Juso Ontology
http://rdfs.co/juso/

Juso Ontology is a Web vocabulary for describing geographical addresses and features. @en
juso.kr - South Korea Extension to Juso Ontology
http://rdfs.co/juso/kr/

South Korea Extension to Juso Ontology is a Web vocabulary that extends Juso Ontology to describe geographical addresses and features in South Korea. @en
ruto - Test-Driven Data Debugging Ontology
http://rdfunit.aksw.org/ns/core#

The Databugger ontology describes concepts used in Databugger, a test driven data-debugging framework that can run automatically generated (based on a schema) and manually generated test cases against an endpoint. @en
isoadr - ISO19160-1:2015 Address ontology
http://reference.data.gov.au/def/ont/iso19160-1-address

This ontology is a version of the ISO TC211, Group for Ontology Management (GOM)'s OWL ontology interpretation of the ISO19160-1:2015 "Addressing -- Part 1: Conceptual model" standard (see https://www.iso.org/standard/61710.html) taken from that ontology's source code, published at https://github.com/ISO-TC211/GOM/tree/master/isotc211_GOM_harmonizedOntology/19160-1/2015. @en
odrs - Open Data Rights Statement Vocabulary
http://schema.theodi.org/odrs

A vocabulary that supports the publication of Open Data by providing the means to capture machine-readable "rights statements", e.g. the licensing information, copyright notices and attribution requirements that are associated with the publication and re-use of a dataset. @en
dqc - The Data Quality Constraints Library
http://semwebquality.org/ontologies/dq-constraints

This RDF document contains a library of data quality constraints represented as SPARQL query templates based on the SPARQL Inferencing Framework (SPIN). The data quality constraint templates are especially useful for the identification of data quality problems during data entry and for periodic quality checks during data usage. @en
frappe - FraPPE: Frame, Pixel, Place, Event vocabulary
http://streamreasoning.org/ontologies/frappe#

FraPPE is a vocabulary to enable Visual Analytics operations on geo-spatial time varying data. By enabling Visual Analytics instruments FraPPE ease the capture, correlation and comparison operations on geo-spatial data from different sources evolving over time @en
voag - Vocabulary Of Attribution and Governance
http://voag.linkedmodel.org/schema/voag

VOAG is intended to specify licensing, attribution, provenance and governance of an ontology. @en
rooms - Buildings and Rooms Vocabulary
http://vocab.deri.ie/rooms

A simple vocabulary for describing the rooms in a building. @en
tao - Trust Assertion Ontology
http://vocab.deri.ie/tao

TAO is a light-weight vocabulary to describe asserted user’s subjective trust values. @en
geod - Administrative vocabulary for Norway
http://vocab.lenka.no/geo-deling

Vocabulary describing the administrative subdivision of Norway @en
arp - Arpenteur Ontology
http://www.arpenteur.org/ontology/Arpenteur.owl

Arpenteur ontology is dedicated to photogrammetry, archeology and oceanology communities in order to perform tasks such as image processing, photogrammetry and modelling. @en
rml-fnml - RML-FNML
http://w3id.org/rml/fnml/

RML-FNML provides data transformations for generating knowledge graphs from (semi-)structured data @en
rml-io - RML-IO: Source and Target
http://w3id.org/rml/io/

RML-IO enables the description of input data sources and target output data serialization @en
rml-star - RML-star
http://w3id.org/rml/star/

RML module for generating RDF-star graphs @en

1 2 345 6...8

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

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French (13)
Spanish (6)
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Chinese (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)