A new framework for taxonomy discovery from text

Abstract

Ontology learning from text is considered as an appealing and a challenging approach to address the shortcomings of the hand-crafted ontologies. In this paper, we present OLEA, a new framework for ontology learning from text. The proposal is a hybrid approach combining the pattern-based and the distributional approaches. It addresses key issues in the area of ontology learning: low recall of the pattern-based approach, low precision of the distributional approach, and finally ontology evolution. Preliminary experiments performed at each stage of the learning process show the pros and cons of the proposal. © 2008 Springer-Verlag Berlin Heidelberg.