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

152

results

How to

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
tac - TripleAccessControl Ontology
http://ns.bergnet.org/tac/0.1/triple-access-control

The TripleAccessControl Ontology is an extension to WebAccessControl Ontology to describe access control rules for RDF triples. @en
sql - SQL Abstract Syntax Trees Vocabulary
http://ns.inria.fr/ast/sql#

The SQL AST Vocabulary allows SQL code abstract syntax trees to be published in RDF. @en
s4ac - Social Semantic SPARQL Security For Access Control Ontology
http://ns.inria.fr/s4ac/v2

A lightweight vocabulary to create fine-grained access control policies for Linked Data. @en
ludo-xapi - Experience API (xAPI)
http://ns.inria.fr/ludo/v1/xapi

This is an OWL formalization of the statements defined in the Experience API (xAPI) specification. It is a module of the LUDO ontology. @en
nrv - Normative Requirements Vocabulary
http://ns.inria.fr/nrv

An OWL vocabulary for describing normative requirements. @en
prissma - Presentation of Resources for Interoperable Semantic and Shareable Mobile Adaptability
http://ns.inria.fr/prissma/v2#

PRISSMA (Presentation of Resources for Interoperable Semantic and Shareable Mobile Adaptability) is a domain-independent vocabulary for displaying Web of Data resources in mobile environments. @en
deps - DOAP Dependencies
http://ontologi.es/doap-deps#

An extension to DOAP for describing a project's requirements @en
og - Open Graph Protocol Vocabulary
http://ogp.me/ns

The Open Graph protocol enables any web page to become a rich object in a social graph. For instance, this is used on Facebook to enable any web page to have the same functionality as a Facebook Page. @en
dbug - DOAP Bugs
http://ontologi.es/doap-bugs#

An extension of DOAP for the description of bugs @en
dbowl - Relational to Ontology Mapping Primitive
http://ontology.cybershare.utep.edu/dbowl

Primitive ontology for database to Semantic Web mapping which subsumes classes that represent mappings to explicit OWL constructs, such as OWL class, object property, data property, etc. Classes in this ontology are populated by individuals representing components of the database schema being mapped. @en
open311 - Open 311 Ontology: An Ontology for publishing a city's non-emergency events.
http://ontology.eil.utoronto.ca/open311.owl

Open 311 Ontology This ontology generalizes the concepts that appear in 311 open data files published by several cities (Toronto, New York, Chicago, Vancouver) across North America. It provides a generis representation of 311 data that other cities can map their data onto and be used as a means of achieving interoperability. @en
irao - Informatics Research Artifacts Ontology
http://ontology.ethereal.cz/irao

Informatics Research Artifacts Ontology, described using W3C RDF Schema and the Web Ontology Language OWL. @en

123 4...11

Selected vocabularies

Patterns?:	type name both
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)