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

122

results

How to

acrt - Agent Certification Ontology
http://privatealpha.com/ontology/certification/1#

This document specifies a vocabulary for asserting the existence of official endorsements or certifications of agents, such as people and organizations. @en
of - Open Fridge vocabulary
http://owlrep.eu01.aws.af.cm/fridge

Ontology for Open Fridge project @en
dqm - The Data Quality Management Vocabulary
http://purl.org/dqm-vocabulary/v1/dqm

The Data Quality Management Vocabulary - An Ontology for Data Requirements Management, Data Quality Monitoring, Data Quality Assessment, and Data Cleansing @en
coo - Car Options Ontology
http://purl.org/coo/ns#

The COO provides a vocabulary for exposing available configuration options for car models. It allows indicating choices that can be made as well as compatibility, dependency, and inclusion information. The ontology imports and extends the GoodRelations ontology for e-commerce @en
daq - Dataset Quality Vocabulary
http://purl.org/eis/vocab/daq#

Quality metrics can be (in principle) calculated on various forms of data (such as datasets, graphs, set of triples etc...). This vocabulary allow the owner/user of such RDF data to calculate metrics on multiple (and different) resources. @en
inno - Ontology for Innovation
http://purl.org/innovation/ns

One key use case for this ontology is to facilitate the matching of needs and innovations. @en
opmv - Open Provenance Model Vocabulary
http://purl.org/net/opmv/ns#

OPMV, the Open Provenance Model Vocabulary, provides terms to enable practitioners of data publishing to publish their data responsibly. @en
prv - Provenance Vocabulary Core Ontology
http://purl.org/net/provenance/ns#

The Provenance Vocabulary Core Ontology provides the main classes and properties required to describe provenance of data on the Web. @en
prvt - Provenance Vocabulary types
http://purl.org/net/provenance/types#

Extends the Provenance Vocabulary by defining subclasses of the types of provenance elements introduced in the core ontology. @en
ofrd - Fridge and Freezer Vocabulary
http://purl.org/opdm/refrigerator#

Vocabulary to describe fridges and freezers @en
pav - Provenance, Authoring and Versioning
http://purl.org/pav/

PAV is a lightweight ontology for tracking Provenance, Authoring and Versioning. PAV specializes the W3C provenance ontology PROV-O in order to describe authorship, curation and digital creation of online resources. @en
vvo - Volkswagen Vehicles Ontology
http://purl.org/vvo/ns#

A vocabulary for describing Volkswagen-specific features of automobiles and their components @en
cs - Changeset
http://purl.org/vocab/changeset/schema

This vocabulary defines a set of terms for describing changes to resource descriptions. @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

123 4...9

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (122)
property/class >
agent >

Language

English (93)
German (9)
Spanish (6)
French (5)
en (5)
Dutch (2)
Italian (2)
Catalan (1)
Chinese (1)
Czech (1)
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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)