Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Ontology Analysis Based on Ontology Design Patterns
Año:2009
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
The so-called Ontology Design Patterns (ODPs), which have been defined as solutions to ontological design problems, are of great help to developers when modelling ontologies since these patterns provide a development guide and improve the quality of the resulting ontologies. However, it has been demonstrated that, in many cases, developers encounter difficulties when they have to reuse ontology design patterns and include errors in the modelling. Thus, to avoid errors in ontology modelling, this paper proposes classifying errors into two types: (1) errors related to existing ODPs, called anti-patterns, and (2) errors not related to existing ODPs, called worst practices. This classification is the result of analysing a set of ontologies which come from an academic experiment. In addition, the paper presents a general classification of the worst practices found and a set of worst practice examples. Finally, the paper shows an example of how the aforementioned worst practices could be related among them.
Internacional
Si
Nombre congreso
WOP 2009 ¿ Workshop on Ontology Patterns at the 8th International Semantic Web Conference (ISWC 2009)
Tipo de participación
960
Lugar del congreso
Washington, DC
Revisores
Si
ISBN o ISSN
1613-0073
DOI
Fecha inicio congreso
25/10/2009
Fecha fin congreso
25/10/2009
Desde la página
155
Hasta la página
162
Título de las actas
Proceedings of the WOP 2009. CEUR Workshop Proceedings
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Maria Poveda Villalon (UPM)
  • Autor: M. Carmen Suarez de Figueroa Baonza (UPM)
  • Autor: Asuncion de Maria Gomez Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group (LIA). Laboratorio Inteligencia Artificial. Grupo de Ingeniería Ontológica
  • Departamento: Inteligencia Artificial
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