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

205

results

How to

okh - Open Know How (OKH) ontology
https://w3id.org/oseg/ont/okh

Used for indexing, searching and comparing Open Source Hardware projects @en
nno - The Neural Network Ontology
https://w3id.org/nno/ontology

This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @en
pep - Process Execution ontology.
https://w3id.org/pep/

The process execution ontology is a proposal for a simple extension of both the [W3C Semantic Sensor Network](https://www.w3.org/TR/vocab-ssn/) and the [Semantic Actuator Network](https://www.irit.fr/recherches/MELODI/ontologies/SAN.owl) ontology cores. @en
pghdprovo - Patient Generated Health Data Provenance Ontology
https://w3id.org/pghdprovo

Patient Generated Health Data (PGHD) refer to health data collected by patient or their relatives. This ontology seeks to capture information about the data, the provenance and data quality associated with PGHD shared with an EHR. @en
seas-stats - The SEAS Statistics ontology.
https://w3id.org/seas/StatisticsOntology

This ontology defines common evaluation interpretation concepts for statistics. @en
seas-eval - The SEAS Evaluation ontology
https://w3id.org/seas/EvaluationOntology

The Evaluation ontology describes evaluation of [`seas:Property`ies](https://w3id.org/seas/Property). There may be: - direct evaluations, or - qualified evaluations. @en
rbdoc - RiverBench documentation ontology
https://w3id.org/riverbench/schema/documentation

Ontology with metadata needed to generate documentation of datasets, distributions, profiles, etc. in RiverBench @en
rb - RiverBench metadata ontology
https://w3id.org/riverbench/schema/metadata

Ontology for describing datasets and profiles in the RiverBench benchmark suite. @en
saont - The System Accountability Ontology
https://w3id.org/sao

An ontology to model accountability of generic systems. @en
rains - The RAInS Ontology
https://w3id.org/rains

An ontology to model accountability of AI systems which use machine learning. @en
pko - Procedural Knowledge Ontology (PKO)
https://w3id.org/pko

The Procedural Knowledge Ontology (PKO) addresses the Procedural Knowledge (PK) domain, and models procedures, their executions, and related resources and agents. @en
swemls - Semantic-Web Machine Learning System (SWeMLS) Ontology
https://w3id.org/semsys/ns/swemls

An ontology to describe a Semantic-Web Machine Learning System (SWeMLS) @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
usability - Usability
https://w3id.org/usability

Ontology 'Usability' created to describe and store information about interactions of user with a software user interface @en

1...11 121314

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)