Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence

Manzano-Macho, D.; Gómez-Pérez, A. y Borrajo Millán, Daniel (2008). Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence. En: "Sixth International Language Resources and Evaluation (LREC'08)", 28-30 may 2008, Marrakech, Morocco. ISBN 2-9517408-4-0.

Descripción

Título: Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence
Autor/es:
  • Manzano-Macho, D.
  • Gómez-Pérez, A.
  • Borrajo Millán, Daniel
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: Sixth International Language Resources and Evaluation (LREC'08)
Fechas del Evento: 28-30 may 2008
Lugar del Evento: Marrakech, Morocco
Título del Libro: Proceedings of the Sixth International Language Resources and Evaluation (LREC'08)
Fecha: Mayo 2008
ISBN: 2-9517408-4-0
Materias:
Palabras Clave Informales: oeg
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Grupo Investigación UPM: Ontology Engineering Group – OEG
Licencias Creative Commons: Ninguna

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (599kB) | Vista Previa

Resumen

Acquiring knowledge from theWeb to build domain ontologies has become a common practice in the Ontological Engineering field. The vast amount of freely available information allows collecting enough information about any domain. However, the Web usually suffers a lack of structure, untrustworthiness and ambiguity of the content. These drawbacks hamper the application of unsupervised methods of building ontologies demanded by the increasingly popular applications of the Semantic Web. We believe that the combination of several processing mechanisms and complementary information sources may potentially solve the problem. The analysis of different sources of evidence allows determining with greater reliability the validity of the detected knowledge. In this paper, we present GALEON (General Architecture for Learning Ontologies) that combines sources and processing resources to provide complementary and redundant evidence for making better estimations about the relevance of the extracted knowledge and their relationships. Our goal in this paper is to show how combining several information sources and extraction mechanisms is possible to build a taxonomy of concepts with a higher accuracy than if only one of them is applied. The experimental results show how this combination notably increases the precision of the obtained results with minimum user intervention.

Más información

ID de Registro: 6550
Identificador DC: http://oa.upm.es/6550/
Identificador OAI: oai:oa.upm.es:6550
URL Oficial: http://www.lrec-conf.org/proceedings/lrec2008/
Depositado por: Dr Oscar Corcho
Depositado el: 30 Mar 2011 10:43
Ultima Modificación: 20 Abr 2016 15:48
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM