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

185

results

How to

sdmx - SDMX-RDF vocabulary
http://purl.org/linked-data/sdmx

This vocabulary extends the data cube vocabulary to support publication of statistical data in RDF, using an information model based on SDMX @en
sdmx-code - SDMX Code
http://purl.org/linked-data/sdmx/2009/code

Defines subclasses and instances of codes used in SDMX, such as currencies, decimals, frequencies ... @en
limo - Linked Statistical Models Vocabulary
http://purl.org/limo-ontology/limo/

A Vocabulary for Incorporating Predictive Models into the Linked Data Web @en
sdmx-dimension - SDMX Dimension
http://purl.org/linked-data/sdmx/2009/dimension

Defines dimensions for the statistical "cubes" defined by SDMX @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
ipo - IPO - Issue Procedure Ontology
http://purl.org/ipo/core

Vocabulary for describing issues (or problems) and corresponding symptoms and solutions to a broad variety of contexts. It is intended to provide a generic, reusable core ontology that can be extended or specialized for use in domain-specific situations, aimed at supporting linked data publishing. The solutions are represented by procedures, which are possible workflows for solving corresponding issues. @en
co - Counter Ontology
http://purl.org/ontology/co/core#

The Counter Ontology specification provides basic concepts and properties for describing a general counter concept and some important sub counters @en
dvia - The visualization vocabulary for LOD applications
http://purl.org/ontology/dvia

dvia is an RDF vocabulary designed to facilitate interoperability between visualization applications published on the Web. @en
reco - RECommendations Ontology
http://purl.org/reco#

The RECO ontology defines the vocabulary for representing preferences-as-constraints and preferences-as-ratings as RDF graphs. This lightweight vocabulary provides domain-independent means to describe user profiles in a coherent and context-aware way. RECO has been designed as an extension of both Friend-Of-A-Friend (FOAF) and Who Am I! (WAI) ontologies. @en
rss - Vocabulary for Rich Site Summary (RSS) 1.0
http://purl.org/rss/1.0

RDF Schema declaration for Rich Site Summary (RSS) 1.0 @en
summa - SUMMA Vocabulary
http://purl.org/voc/summa/

The SUMMA Vocabulary enables to request and represent summarized RDf resources. It should be used in context to the SUMMA API definition. @en
toco - Toucan Ontology
http://purl.org/toco/

A ontology developed for telecommunciation network systems with hybrid technologies, e.g., WiFi, LiFi, LTE, 2G/3G, optical, etc. @en
xapi - xAPI Controlled Vocabulary Ontology
http://purl.org/xapi/ontology#

This ontology contains a list of the classes and properties that can be used when for creating and publishing a controlled vocabulary dataset for xAPI communities of practice. @en
sou - QUDT VOCAB Systems of Units Release 2.1.34
http://qudt.org/2.1/vocab/sou

QUDT VOCAB Systems of Units Release 2.1.34 @en
qudt - Quantities, Units, Dimensions and Types
http://qudt.org/schema/qudt

The QUDT, or 'Quantity, Unit, Dimension and Type' collection of ontologies define the base classes properties, and restrictions used for modeling physical quantities, units of measure, and their dimensions in various measurement systems. @en

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Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

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German (5)
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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)