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

155

results

How to

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
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
aws - Ontology for Meteorological sensors
http://purl.oclc.org/NET/ssnx/meteo/aws

This ontology is part of the Agriculture Meteorology example showcasing the ontology developed by the W3C Semantic Sensor Networks incubator group (SSN-XG). It is published here in order to generalize the potential usage and the alignment with other standardization efforts of the SSN ontology. @en

123 4...11

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (155)
property/class >
agent >

Language

English (128)
French (6)
German (6)
Portuguese (3)
Italian (2)
Japanese (2)
Spanish (2)
Korean (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)