The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a 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 system.
Terminological Ontology Learning and Population using Latent Dirichlet Allocation / Francesco, Colace; Massimo De, Santo; Luca, Greco; Moscato, Vincenzo; Picariello, Antonio. - (2014), pp. 196-203. (Intervento presentato al convegno The 20th International Conference on Distributed Multimedia Systems (DMS 2014) tenutosi a Wyndham Pittsburgh University Center, Pittsburgh, USA nel 27 - 29 August, 2014).
Terminological Ontology Learning and Population using Latent Dirichlet Allocation
MOSCATO, VINCENZO;PICARIELLO, ANTONIO
2014
Abstract
The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a 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 system.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
