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

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.