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

126

results

How to

dsv - Data Specification Vocabulary
https://w3id.org/dsv#

Data Specification Vocabulary (DSV) is a vocabulary for describing semantic data specifications, namely vocabularies and application profiles. @en
eeo - Experimental Evaluation Ontology
https://w3id.org/eeo

An ontology to describe experiments, evaluations and their relation. @en
puv - Parameter Usage Vocabulary ontology
https://w3id.org/env/puv

A simple ontology which implements the Parameter Usage Vocabulary semantic model, as described at https://github.com/nvs-vocabs/P01 @en
eppl - The EP-Plan ontology
https://w3id.org/ep-plan

PROV extension for linking Plans and parts of plans to their respective executions. @en
chameo - CHAracterisation MEthodology Ontology
https://w3id.org/emmo/domain/characterisation-methodology/chameo

CHAMEO is a domain ontology designed to model the common aspects across the different characterisation techniques and methodologies. @en
ishi - Ishikawa diagram ontology
https://w3id.org/ishikawa-diagram-ontology

The Ishikawa ontology aims to provide a data and view model to manage data encoded in Ishikawa diagrams which are also known as fishbone or cause and effect diagram (CED). Ishikawa diagrams result from (iterative) workshops. Thus, the ontology includes the basic modelling of workshops to create Ishikawa diagrams. @en
icon - ICON Ontology
https://w3id.org/icon/ontology/

The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. @en
loin - Level of Information Need (LOIN) Ontology
https://w3id.org/loin

The Level of Information Need (LOIN) Ontology is defined for specifying information requirements for delivery of data in a buildings' life cycle. The LOIN ontology is based on the standard BS EN 17412-1 (2020). Furthermore, it is extended with vocabulary for connecing Information Delivery Specifications (IDS) and Information containers for linked document delivery (ICDD) as per ISO 21597-1 (2020). @en
ontouml - OntoUML Metamodel Vocabulary
https://w3id.org/ontouml

A reference implementation of the OntoUML metamodel in OWL. @en
sdont - The Software Description Ontology
https://w3id.org/okn/o/sd

An ontology for describing software and their links to inputs, outputs and variables. The ontology extends schema.org and codemeta vocabularies @en
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
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

1...5 678 9

Selected vocabularies

Patterns?:	type name both
Flatten?:	yes no

Type

vocabulary (126)
property/class >
agent >

Language

English (92)
en (5)
French (3)
German (3)
Portuguese (3)
Czech (1)
Spanish (1)

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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)