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

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

Description

Title: Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence
Author/s:
  • Manzano-Macho, D.
  • Gómez-Pérez, A.
  • Borrajo Millán, Daniel
Item Type: Presentation at Congress or Conference (Article)
Event Title: Sixth International Language Resources and Evaluation (LREC'08)
Event Dates: 28-30 may 2008
Event Location: Marrakech, Morocco
Title of Book: Proceedings of the Sixth International Language Resources and Evaluation (LREC'08)
Date: May 2008
ISBN: 2-9517408-4-0
Subjects:
Freetext Keywords: oeg
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
UPM's Research Group: Ontology Engineering Group – OEG
Creative Commons Licenses: None

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Abstract

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.

More information

Item ID: 6550
DC Identifier: http://oa.upm.es/6550/
OAI Identifier: oai:oa.upm.es:6550
Official URL: http://www.lrec-conf.org/proceedings/lrec2008/
Deposited by: Dr Oscar Corcho
Deposited on: 30 Mar 2011 10:43
Last Modified: 20 Apr 2016 15:48
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