Enhanced semantic automatic ontology enrichment

With the fast growing development of the Web, the adoption of ontologies to improve the exploitation of information resources, is already heralded as a promising model of representation. However, the relevance of information that they contain requires regular updating, and specifically, the addition of new knowledge. Recently, new research approaches were defined in order to automatically enrich ontology. Usually they extensively use either statistical models or experts to provide the relevance and placement of new concepts. Unfortunately these approaches suffer the following drawback: The detection of new elements and position in ontology are not automatically established, and lack of use of semantic or syntactic information to extract relations between concepts. In this paper, we present an approach for ontology enrichment based on two steps where a novel effective filtering step is utilized. First we extract the correlation between terms of a learning dataset using the generation of association rules. Second we retain the relevant new concepts using an extracted semantic information. The suggested approach was tested on an ontology of mechanical industry competencies. Experiments were performed on real data, which show the usefulness of our approach.