A Learning-Based Model for Semantic Mapping from Natural Language Questions to OWL

  • Authors:

  • Mingxia Gao;Jiming Liu;Ning Zhong;Chunnian Liu;Furong Chen

  • Affiliations:

  • The International WIC Institute, Beijing University of Technology, Beijing, China;The International WIC Institute, Beijing University of Technology, Beijing, China and Department of Computer Science, Hong Kong Baptist University Hong Kong SAR,;The International WIC Institute, Beijing University of Technology, Beijing, China and Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan;The International WIC Institute, Beijing University of Technology, Beijing, China;R&D Center TravelSky Technology Limited,

  • Venue:
  • RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
  • Year:
  • 2007

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Abstract

One of key problems in implementing a dynamic interface between human and agents is how to do semantic mapping from natural language questions to OWL. The paper views the task as a two-class classification problem. A pair of question variable and OWL element is a sample. Two classes of "Matched" and "Unmatched" explain two relations between the question variable and the OWL element in a given sample. Building appropriate semantic mapping is the same as classifying the sample to a "Matched" class by an effective machine learning method and a trained model. Two types of features of samples are selected. Syntactical features denote the syntactical structure of a given sample. Semantic features present multiple relations between the question variable and the OWL element in one sample. Preliminary experimental results show that the sum precision of the learning-based model is better than that of the constraints-based method.