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Memorias de investigación
Artículos en revistas:
Methodological Guidelines for Reusing General Ontologies
Año:2013
Áreas de investigación
  • Informática
Datos
Descripción
Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have.
Internacional
Si
JCR del ISI
Si
Título de la revista
Data & Knowledge Engineering
ISSN
0169-023X
Factor de impacto JCR
1,519
Información de impacto
Volumen
86
DOI
Número de revista
Desde la página
242
Hasta la página
275
Mes
JULIO
Ranking
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS: Q2
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Mariano Fernández López (CEU San Pablo)
  • Autor: Asuncion de Maria Gomez Perez (UPM)
  • Autor: M. Carmen Suarez de Figueroa Baonza (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group
  • Departamento: Inteligencia Artificial
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