Terminological ontology learning and population using latent Dirichlet allocation

TLDR: An ontology learning and population system that combines both statistical and semantic methodologies is presented that achieves good performances on standard datasets.

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 approach. HighlightsA graph of terms can be effectively used for ontology building.Such a graph is extracted from documents thanks to a LDA based methodology.Ontology learning involves the use of annotated lexicons (WordNet).Proposed method achieves good performances on standard datasets.