Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence
Título de la tesis:
Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence
Autor/es:
Manzano-Macho, D. - Gómez-Pérez, A. - Borrajo, D.
Tipo de documento:
Ponencia en Congreso o Jornada (Artículo)
Universidad:
Facultad de Informática (UPM)
Departamento:
Inteligencia Artificial
Idioma:
Palabras clave:
oeg
Fecha de la defensa:
Mayo 2008-01-01
Notas:
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 poten...
Valoración: