The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is stil la lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach.

Terminological ontology learning and population using latent Dirichlet allocation

COLACE, Francesco;DE SANTO, Massimo;GRECO, LUCA;
2014

Abstract

The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is stil la lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4539657
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