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

137

results

How to

geof - Geo Features
http://www.mindswap.org/2003/owl/geo/geoFeatures20040307.owl

This ontology contains geographic feature classes and associated properties including classes and properties for describing the spatial location of the geographic feature. The classes and properties have been defined based on an ESRI dataset. @en
ceo - Consumer Electronics Ontology
http://www.ebusiness-unibw.org/ontologies/consumerelectronics/v1

An Ontology for Consumer Electronics Products and Services @en
ti - The Time Interval Pattern
http://www.ontologydesignpatterns.org/cp/owl/timeinterval.owl

This pattern is extracted from DOLCE-UltraLite by partial clone of elements and expansion. Two datatype properties have been added which allow to express the boundaries of the time interval. @en
ostop - Ordnance Survey Topography Ontology
http://www.ordnancesurvey.co.uk/ontology/Topography/v0.1/Topography.owl

The general purpose of this ontology is to provide a library of high level concepts that are used by the other modules within the whole OS Topographic ontology. The ontology also describes the relationships and instances common to more than one module. @en
traffic - Road Traffic Management
http://www.sensormeasurement.appspot.com/ont/transport/traffic

An ontology that describes the management of the traffic in a straight road with two lanes, both in the same direction. @en
ncal - NEPOMUK Calendar Ontology
http://www.semanticdesktop.org/ontologies/2007/04/02/ncal

The NEPOMUK Calendaring Ontology intends to provide vocabulary for describing calendaring data (events, tasks, journal entries) which is an important part of the body of information usually stored on a desktop. It is an adaptation of the ICALTZD ontology created by the W3C RDF Calendar Task Force of the Semantic Web Interest Group in the Semantic Web Activity. @en
geo - WGS84 Geo Positioning
http://www.w3.org/2003/01/geo/wgs84_pos

A vocabulary for representing latitude, longitude and altitude information in the WGS84 geodetic reference datum. @en
time - Time Ontology
http://www.w3.org/2006/time

This vocabulary defines temporal entities such as time intervals, their properties and relationships. @en
cal - Internet Calendaring and Scheduling Core Object Specification
http://www.w3.org/2002/12/cal/ical

A vocabulary for description of events and calendars @en
tzont - Time Zone Ontology
http://www.w3.org/2006/timezone

A vocabulary to describe time zones and their geographical coverage. @en
te - Time Entry
http://www.w3.org/2006/time-entry

An entry sub-ontology of time (OWL-Time). @en
locn - ISA Programme Location Core Vocabulary
http://www.w3.org/ns/locn

The ISA Programme Location Core Vocabulary provides a minimum set of classes and properties for describing any place in terms of its name, address or geometry. The vocabulary is specifically designed to aid the publication of data that is interoperable with EU INSPIRE Directive. @en
atts - Air Traffic Temporal and Spacial Vocabulary
https://data.nasa.gov/ontologies/atmonto/general#

Defines temporal / spatial concepts and general-purpose datastructures @en
rma - Verisav RMA / Ticketing Vocabulary
https://ns.verisav.fr/rma#

Vocabulary for Return Merchandise Authorization (RMA) and service ticket management, standardizing communication between consumers, retailers, and repairers. @en
dpp - Verisav Digital Product Passport Vocabulary
https://ns.verisav.fr/dpp#

Vocabulary for Digital Product Passports (DPP) managing product lifecycle, warranties, repairs, and compliance with EU regulations (ESPR EU 2024/1781). Aligned with GS1 Digital Link standards, including GTIN, GLN, granularity (model/batch/serial) and compound identifier support. @en

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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)