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

164

results

How to

semsur - The Semantic Survey Ontology (semsur)
http://purl.org/SemSur/

SemSur, the Semantic Survey Ontology, is a core ontology for describing individual research problems, approaches, implementations and evaluations in a structured, comparable way. @en
cold - 'Configuration as Linked Data' ontology
http://purl.org/configurationontology

Ontology for the description of customizable products. It models the configuration process as the traversal of a graph of partially defined products, or "Configurations" @en
coo - Car Options Ontology
http://purl.org/coo/ns#

The COO provides a vocabulary for exposing available configuration options for car models. It allows indicating choices that can be made as well as compatibility, dependency, and inclusion information. The ontology imports and extends the GoodRelations ontology for e-commerce @en
inno - Ontology for Innovation
http://purl.org/innovation/ns

One key use case for this ontology is to facilitate the matching of needs and innovations. @en
gr - The GoodRelations Ontology for Semantic Web-based E-Commerce
http://purl.org/goodrelations/v1

The GoodRelations ontology provides the vocabulary for annotating e-commerce offerings (1) to sell, lease, repair, dispose, or maintain commodity products and (2) to provide commodity services. @en
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
sdmx-dimension - SDMX Dimension
http://purl.org/linked-data/sdmx/2009/dimension

Defines dimensions for the statistical "cubes" defined by SDMX @en
limo - Linked Statistical Models Vocabulary
http://purl.org/limo-ontology/limo/

A Vocabulary for Incorporating Predictive Models into the Linked Data Web @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
qb - The data cube vocabulary
http://purl.org/linked-data/cube

This vocabulary allows multi-dimensional data, such as statistics, to be published in RDF. @en
wf-invoc - Workflow Invocation Ontology
http://purl.org/net/wf-invocation

Wf-invoc is a simple profile of the P-plan ontology to describe how workflow steps are invoked within a workflow execution. @en
wfm - The Workflow Motif Ontology
http://purl.org/net/wf-motifs

Ontology for describing Workflow Motifs. Workflow Motifs outline the kinds of data-intensive activities that are observed in workflows (data-operation motifs) and the different manners in which activities are implemented within workflows (workflow-oriented motifs). @en
p-plan - The P-PLAN Ontology
http://purl.org/net/p-plan#

The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. @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

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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)