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Memorias de investigación
Ponencias en congresos:
A MAUT Approach for Reusing Ontologies
Año:2012
Áreas de investigación
  • Ingenierías
Datos
Descripción
Knowledge resource reuse has become a popular approach within the ontology engineering field, mainly because it can speed up the ontology development process, saving time and money and promoting the application of good practices. The NeOn Methodology provides guidelines for reuse. These guidelines include the selection of the most appropriate knowledge resources for reuse in ontology development. This is a complex decision-making problem where different conflicting objectives, like the reuse cost, understandability, integration workload and reliability, have to be taken into account simultaneously. GMAA is a PC-based decision support system based on an additive multi-attribute utility model that is intended to allay the operational difficulties involved in the Decision Analysis methodology. The paper illustrates how it can be applied to select multimedia ontologies for reuse to develop a new ontology in the multimedia domain. It also demonstrates that the sensitivity analyses provided by GMAA are useful tools for making a final recommendation.
Internacional
Si
Nombre congreso
Workshop on Data-Driven Decision Guidance and Support Systems
Tipo de participación
960
Lugar del congreso
Washington, USA.
Revisores
Si
ISBN o ISSN
978-1-4673-1640-8
DOI
Fecha inicio congreso
01/04/2012
Fecha fin congreso
05/04/2012
Desde la página
34
Hasta la página
40
Título de las actas
Proceeding Workshop on Data-Driven Decision Guidance and Support Systems
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Antonio Jimenez Martin (UPM)
  • Autor: M.C. Suárez
  • Autor: Alfonso Mateos Caballero (UPM)
  • Autor: M. Fernández
  • Autor: Asuncion de Maria Gomez Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Grupo de Investigación: Ontology Engineering Group
  • Departamento: Inteligencia Artificial
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