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LREC
2008

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

8 years 11 months ago
Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence
Acquiring knowledge from the Web 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 ...
David Manzano-Macho, Asunción Gómez-
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where LREC
Authors David Manzano-Macho, Asunción Gómez-Pérez, Daniel Borrajo
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