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

183

results

How to

dogont - Ontology Modeling for Intelligent Domotic Environments
http://elite.polito.it/ontologies/dogont.owl

The DogOnt ontology supports device/network independent description of houses, including both controllable and architectural elements. @en
earth - The Linked Earth Ontology
http://linked.earth/ontology#

The Linked Earth Ontology aims to provide a common vocabulary for annotating paleoclimatology data @en
trao - Transport Administration Ontology
http://linkeddata.finki.ukim.mk/lod/ontology/tao#

Transport Administration Ontology (TAO) for describing data from Swedish Transport Administration Web site. @en
veo - Vehicle Emissions Ontology
http://linkeddata.finki.ukim.mk/lod/ontology/veo#

An ontology for describing vehicles and their emissions. @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
kees - KEES Ontology
http://linkeddata.center/kees/v1

KEES (Knowledge Exchange Engine Schema ) ontology describes a knowledge base configuration in terms of ABox and TBox statements together with their accrual and reasoning policies. This vocabulary is designed to drive automatic data ingestion in a graph database according KEES and Linked (Open) Data principles. @en
cpmeta - Ontology of Integrated Carbon Observation System (ICOS)
http://meta.icos-cp.eu/ontologies/cpmeta/

OWL vocabulary describing metadata of two environmental research infrastructures: ICOS and SITES @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
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
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

123 4...13

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (183)
property/class >
agent >

Language

English (145)
French (8)
Spanish (8)
German (5)
Italian (5)
Portuguese (3)
Chinese (1)
Dutch (1)
Finnish (1)
Japanese (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)