An entailment-based question answering system over semantic web data

Authors:
Shiyan Ou;Zhenyuan Zhu
Affiliations:
Department of Information Management, Nanjing University, China;Department of Information Management, Nanjing University, China
Venue:
ICADL'11 Proceedings of the 13th international conference on Asia-pacific digital libraries: for cultural heritage, knowledge dissemination, and future creation
Year:
2011

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Abstract

This paper reports a novel knowledge-based Question Answering (QA) method with the use of Semantic Web technologies and textual entailment recognition. Different from most of ontology-driven QA methods, this method does not perform deep question analysis to transform a natural language question into an ontology-compliant query for answer retrieval. Instead, it performs textual entailment recognition to discover the question template entailed by a user question from the whole machine-generated set and then takes the associated SPARQL query template to produce the complete query for retrieving the answers from the Semantic Web data that subscribe to the same ontology. An evaluation was carried out to assess the accuracy of the QA method, and the results revealed that the generated question templates can cover almost all the user questions and 65.6% of the user questions can be correctly answered with the support of a semantic entailment engine.