A hybrid approach for taxonomy learning from text

Ontology learning from text is considered as an appealing and challeging alternative 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: context-dependency, low recall of the pattern-based approach, low precision of the distributionnal approach, and finally ontology evolution. Experiments performed at each stage of the learning process show the advantages and drawbacks of the proposal.