Data-driven and ontological analysis of Framenet for natural language reasoning

This paper focuses on the improvement of the conceptual structure of FrameNet for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, information extraction etc. Ontological analysis supported by data-driven methods is used for axiomatizing, enriching and cleaning up frame relations. The impact of the achieved axiomatization is investigated on recognizing textual entailment.