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

195

results

How to

tribont-core - Core module
https://w3id.org/tribont/core

The goal of this module is to represent the common classes, and object and data properties included in two or more modules of the TribOnt ontology. @en
tido - The Threat Intelligence Decision Ontology
https://w3id.org/tido#

The TIDO ontology can be used to describe the decision processes within the threat intelligence domain @en
tribont-sample - Sample module
https://w3id.org/tribont/sample

The goal of this module is to represent the sample systems , and the undelaying samples, involved in the tribological experiments. @en
tddom - TODODom: Domain Module for Task-Oriented Dialogue management Ontology (TODO)
https://w3id.org/todo/tododom

Module for domain modelling. @en
xbrll - A lightweight XBRL vocabulary
https://w3id.org/vocab/xbrll

An attempt to publish XBRL standard following best practices on the Web. Not an official version from XBRL International and under development @en
vpa - ERA ontology for verified permissions
https://w3id.org/vpa

ERA ontology for verified permissions, as applied in vehicle(type) authorisations, registrations and approvals @en
gleif-elf - Entity Legal Form Ontology
https://www.gleif.org/ontology/EntityLegalForm/

Ontology defining concepts for Entity Legal Forms and their abbreviations by jurisdiction, based on ISO 20275. Though used by Global Legal Entity Identifier Foundation (GLEIF) for Legal Entity Identifier registration, it is more broadly applicable. @en
wfont - Wind Farm Ontology (wfont)
https://w3id.org/wfont

The Wind Farm Ontology (wfont) describes wind farms and their components. It is inspired by the SANDIA Report SAND2009-1171 and DAEKIN project outcomes. It reuses the AffectedBy and EEP (Execution-Executor-Procedure) ontology design patterns to discover sensors or actuators that observe or act on a given quality or feature of interest. @en
hw - Home Weather
https://www.auto.tuwien.ac.at/downloads/thinkhome/ontology/WeatherOntology.owl

Smart home ontology for weather phenomena and exterior conditions @en
vair - Vocabulary of AI Risks
https://w3id.org/vair

VAIR is a taxonomy of AI and risk concepts. @en
w3c-ssn - Semantic Sensor Network Ontology
https://www.w3.org/ns/ssn

This ontology describes sensors and observations, and related concepts. It does not describe domain concepts, time, locations, etc. as these are intended to be included from other ontologies via OWL imports. @en
mso-em - MSO-EM: Ontologies for modelling, simulation, optimization (MSO) and epistemic metadata (EM)
https://www.purl.org/mso-em

MSO-EM is a system of ontologies for documenting the knowledge status of models and data; the aim is to make models and data explainable-AI-ready (XAIR). @en
sur - The Survey Ontology
https://www.w3id.org/survey-ontology

Ontology for surveys based on the Coney data model. @en
sstim
https://w3id.org/sstim
oso
https://w3id.org/earthsemantics/OSO

1...11 1213

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

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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)