Linked Open Vocabularies

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Terms
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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

213

results

How to

eupont - EUPont: an ontology for End User Programming of the IoT
http://elite.polito.it/ontologies/eupont.owl

EUPont is an ontology to model high level rules for Internet of Things End User Programming (IoT-EUP). @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
ldvm - Vocabulary for Linked Data Visualization Model
http://linked.opendata.cz/ontology/ldvm/

Vocabulary for Linked Data Visualization Model (LDVM) serves for description and configuration of components and pipelines according to LDVM @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
lsc - Linked Science Core Vocabulary
http://linkedscience.org/lsc/ns#

LSC, the Linked Science Core Vocabulary, is a lightweight vocabulary providing terms to enable publishers and researchers to relate things in science to time, space, and themes. @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
dcndl - NDL Metadata Terms
http://ndl.go.jp/dcndl/terms/

RDF Schema declaration for Japan NDL Metadata Terms @en
nrv - Normative Requirements Vocabulary
http://ns.inria.fr/nrv

An OWL vocabulary for describing normative requirements. @en
deps - DOAP Dependencies
http://ontologi.es/doap-deps#

An extension to DOAP for describing a project's requirements @en
lsweb - ELSEWeb Data Ontology
http://ontology.cybershare.utep.edu/ELSEWeb/elseweb-data.owl

The ELSEWeb Data ontology provides simple classes for describing datasets. This project provides foundational support for the ELSEWeb project(http://elseweb.cybershare.utep.edu/). website: http://elseweb.cybershare.utep.edu/ontologies email: nvillanuevarosales@utep.edu @en
munc - Meta-Uncertainty
http://ns.inria.fr/munc/

An OWL vocabulary for describing uncertainty metadata. @en
dm2e - DM2E model
http://onto.dm2e.eu/schemas/dm2e

The DM2E model is a specialisation of the Europeana Data Model (EDM) and can be used for the description of manuscripts in the cultural heritage domain. @en
dbug - DOAP Bugs
http://ontologi.es/doap-bugs#

An extension of DOAP for the description of bugs @en

123 4...15

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (213)
property/class >
agent >

Language

English (173)
French (9)
German (8)
Spanish (6)
Italian (5)
Japanese (3)
Portuguese (3)
Chinese (1)
Czech (1)
Dutch (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)