Title: Lightweight domain ontology learning from texts: graph theory-based approach using Wikipedia

Authors: Khalida Bensidi Ahmed; Adil Toumouh; Dominic Widdows

Addresses: Department of Computer Science, Djillali Liabes University, Sidi Bel Abbes, Algeria ' Department of Computer Science, Djillali Liabes University, Sidi Bel Abbes, Algeria ' Microsoft Corp., Seattle, USA

Abstract: Ontology engineering is the backbone of the semantic web. However, the construction of formal ontologies is a tough exercise which requires time and heavy costs. Ontology learning is thus a solution for this requirement. Since texts are massively available everywhere, making up of experts' knowledge and their know-how, it is of great value to capture the knowledge existing within such texts. Our approach is thus the kind of research work that answers the challenge of creating concepts' hierarchies from textual data taking advantage of the Wikipedia encyclopaedia to achieve some good-quality results. This paper presents a novel approach which essentially uses plain text Wikipedia instead of its categorical system and works with a simplified algorithm to infer a domain taxonomy from a graph.

Keywords: lightweight domain ontologies; ontology learning; texts; concept hierarchies; graph normalisation; Wikipedia; graph theory; ontology engineering; semantic web; textual data.

DOI: 10.1504/IJMSO.2014.060323

International Journal of Metadata, Semantics and Ontologies, 2014 Vol.9 No.2, pp.83 - 90

Received: 03 Sep 2012
Accepted: 02 Aug 2013

Published online: 10 Apr 2014 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article