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

141

results

How to

rdf - The RDF Concepts Vocabulary
http://www.w3.org/1999/02/22-rdf-syntax-ns#

This is the RDF Schema for the RDF vocabulary terms in the RDF Namespace, defined in RDF 1.1 Concepts. @en
skos - Simple Knowledge Organization System
http://www.w3.org/2004/02/skos/core

The Simple Knowledge Organization System (SKOS) is a common data model for sharing and linking knowledge organization systems via the Semantic Web. @en
rdfs - The RDF Schema vocabulary
http://www.w3.org/2000/01/rdf-schema#

RDF Schema provides a data-modelling vocabulary for RDF data. RDF Schema is an extension of the basic RDF vocabulary. @en
test - Test Metadata
http://www.w3.org/2006/03/test-description

This ontology aims at defining the Quality Assurance Framework by collecting the test development experience of W3C Working Groups and summarizing the work done about tests and metadata. @en
xsd - XML Schema
http://www.w3.org/2001/XMLSchema

The XML Schema representation @en
owl - The OWL 2 Schema vocabulary
http://www.w3.org/2002/07/owl

This ontology partially describes the built-in classes and properties that together form the basis of the RDF/XML syntax of OWL 2. @en
cert - The Cert Ontology
http://www.w3.org/ns/auth/cert#

Ontology for Certificates and crypto stuff. @en
dcat - Data Catalog Vocabulary
http://www.w3.org/ns/dcat

DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web @en
dqv - Data Quality Vocabulary
http://www.w3.org/ns/dqv

The Data Quality Vocabulary (DQV) is seen as an extension to DCAT to cover the quality of the data, how frequently is it updated, whether it accepts user corrections, persistence commitments etc. When used by publishers, this vocabulary will foster trust in the data amongst developers. @en
skosxl - SKOS eXtension for Labels
http://www.w3.org/2008/05/skos-xl

SKOS-XL defines an extension for the Simple Knowledge Organization System, providing additional support for describing and linking lexical entities. @en
csvw - CSV on the Web Vocabulary
http://www.w3.org/ns/csvw#

This document describes the RDFS vocabulary description used in the Metadata Vocabulary for Tabular Data along with the default JSON-LD Context. @en
earl - Evaluation and Report Language
http://www.w3.org/ns/earl

EARL is a vocabulary, the terms of which are defined across a set of specifications and technical notes, and that is used to describe test results. The primary motivation for developing this vocabulary is to facilitate the exchange of test results between Web accessibility evaluation tools in a vendor-neutral and platform-independent format. It also provides reusable terms for generic quality assurance and validation purposes. @en
mls - Machine Learning Schema
http://www.w3.org/ns/mls

ML-Schema is a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments @en
oa - Open Annotation Data Model
http://www.w3.org/ns/oa#

The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, annotations, using a methodology that conforms to the Architecture of the World Wide Web. This ontology is a non-normative OWL formalization of the textual OA specification at http://www.openannotation.org/spec/core/20130208/index.html @en
prov - W3C PROVenance Interchange
http://www.w3.org/ns/prov#

The namespace name http://www.w3.org/ns/prov# is intended for use with the PROV family of documents that support the interchange of provenance on the web. @en

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Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (141)
property/class >
agent >

Language

English (106)
en (7)
French (5)
German (5)
Portuguese (3)
Spanish (3)
Italian (2)
Japanese (2)
Arabic (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)