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

120

results

How to

osadm - The administrative geography and civil voting area ontology
http://data.ordnancesurvey.co.uk/ontology/admingeo/

An ontology describing the administrative and voting area geography of Great Britain @en
g50k - 50K Gazetteer Vocabulary
http://data.ordnancesurvey.co.uk/ontology/50kGazetteer/

A vocabulary developed to describe the Ordance Survey 50k Gazetteer linked data @en
om - ISO 19156 Observation Model
http://def.seegrid.csiro.au/isotc211/iso19156/2011/observation

An OWL representation of the Observation Schema described in clause 6 of ISO 19156:2011 Geographic Information - Observations and Measurements @en
oml - OWL for Observations
http://def.seegrid.csiro.au/ontology/om/om-lite

An OWL representation of the Observation Schema described in clauses 7-8 of ISO 19156:2011 Geographic Information - Observations and Measurements / OGC Abstract Specification - Topic 20. @en
samfl - OWL for Sampling Features
http://def.seegrid.csiro.au/ontology/om/sam-lite

A simple OWL representation of the Sampling Features Schema described in clauses 9-11 of ISO 19156:2011 Geographic Information - Observations and Measurements @en
gf - OWL representation of ISO 19109 (General Feature Model)
http://def.seegrid.csiro.au/isotc211/iso19109/2005/feature

An OWL representation of part of the General Feature Model described in ISO 19109 and the General Feature Instance model described in Annex C of ISO 19156:2011. @en
md - OWL representation of ISO 19115
http://def.seegrid.csiro.au/isotc211/iso19115/2003/metadata

An OWL representation of parts of the Geographic Metadata model described in ISO 19115:2003 with Corrigendum 2006 - MD Package. @en
nen3610 - NEN 3610 - base model geo-information
http://modellen.geostandaarden.nl/def/nen3610

NEN 3610 - base model geo-information – terms, definitions, relationships and general rules for the exchange of information about earth related spatial objects @en
fiesta-priv - FIESTA-Priv Ontology
http://purl.org/iot/ontology/fiesta-priv#

FIESTA-Priv Ontology @en
iottaxolite - The IoTTaxonomy-lite Taxonomy
http://purl.org/iot/vocab/iot-taxonomy-lite#

The IoT-Taxonomy-lite is adapted from M3-lite taxonomy. This taxonomy is refactored and defines many other concepts such as subclasses of Feature-of-Interest and Quality-of-Observation. @en
fiesta-iot - FIESTA-IoT Ontology
http://purl.org/iot/ontology/fiesta-iot

The FIESTA-IoT ontology takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 ontology), WGS84, IoT-lite, Time, and DUL ontology. @en
m3lite - The Machine-to-Machine Measurement (M3) Lite Ontology
http://purl.org/iot/vocab/m3-lite#

M3 lite taxonomy is designed for the FIESTA-IOT H2020 EU project. We refactor, clean and simplify the M3 ontology designed by Eurecom (Amelie Gyrard). M3 ontology lite is currently aligned with the quantity taxonomy used by several testbeds: SmartSantander (Spain), University of Surrey (United Kingdom), KETI (Korea) and Com4Innov (France). @en
igeo - French Statistical ontology for geolocation
http://rdf.insee.fr/def/geo

Ontology describing geographic entities as seen by the French Statistical Institute @en
juso.kr - South Korea Extension to Juso Ontology
http://rdfs.co/juso/kr/

South Korea Extension to Juso Ontology is a Web vocabulary that extends Juso Ontology to describe geographical addresses and features in South Korea. @en
juso - Juso Ontology
http://rdfs.co/juso/

Juso Ontology is a Web vocabulary for describing geographical addresses and features. @en

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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)