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A Question Answering System Built on Domain Knowledge Base

  • Yicheng LiuEmail author
  • Yu Hao
  • Xiaoyan Zhu
  • Jiao Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

Interactive Question Answering (QA) system is capable of answering users’ questions with managing/understanding the dialogs between human and computer. With the increasing amount of online information, it is highly needed to answer users’ concerns on a specific domain such as health-related questions. In this paper, we proposed a general framework for domain-specific interactive question answering systems which takes advance of domain knowledge bases. First, a semantic parser is generated to parse users’ questions to the corresponding logical forms on basis of the knowledge base structure. Second, the logical forms are translated into query language to further harvest answers from the knowledge base. Moreover, our framework is featured with automatic dialog strategy development which relies on manual intervention in traditional interactive QA systems. For evaluation purpose, we applied our framework to a Chinese interactive QA system development, and used a health-related knowledge base as domain scenario. It shows promising results in parsing complex questions and holding long history dialog.

Keywords

Question Answering Business Logic SPARQL Query Dialog System Question Answering System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., Steedman, M.: Lexical generalization in ccg grammar induction for semantic parsing. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1512–1523. Association for Computational Linguistics (2011)Google Scholar
  2. 2.
    Liang, P., Jordan, M.I., Klein, D.: Learning dependency-based compositional semantics. Computational Linguistics 39, 389–446 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Yahya, M., Berberich, K., Elbassuoni, S., Ramanath, M., Tresp, V., Weikum, G.: Natural language questions for the web of data. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 379–390. Association for Computational Linguistics (2012)Google Scholar
  4. 4.
    Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: Proceedings of ACL (2014)Google Scholar
  5. 5.
    Krishnamurthy, J., Mitchell, T.M.: Weakly supervised training of semantic parsers. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 754–765. Association for Computational Linguistics (2012)Google Scholar
  6. 6.
    Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: EMNLP, pp. 1533–1544 (2013)Google Scholar
  7. 7.
    Cai, Q., Yates, A.: Large-scale semantic parsing via schema matching and lexicon extension. In: ACL (1), Citeseer, pp. 423–433 (2013)Google Scholar
  8. 8.
    Wu, X., Zheng, F., Xu, M.: Topic forest: A plan-based dialog management structure. In: Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001) vol. 1, pp. 617–620. IEEE (2001)Google Scholar
  9. 9.
    Rudnicky, A., Xu, W.: An agenda-based dialog management architecture for spoken language systems. In: IEEE Automatic Speech Recognition and Understanding Workshop, pp. 1–337 (1999)Google Scholar
  10. 10.
    Doshi, F., Roy, N.: Efficient model learning for dialog management. In: 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 65–72. IEEE (2007)Google Scholar
  11. 11.
    Young, S., Schatzmann, J., Weilhammer, K., Ye, H.: The hidden information state approach to dialog management. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007, vol. 4, pp. IV-149. IEEE (2007)Google Scholar
  12. 12.
    Shekarpour, S., Ngonga Ngomo, A.C., Auer, S.: Question answering on interlinked data. In: Proceedings of the 22nd International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 1145–1156 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Tsinghua National Laboratory of Intelligent Technology and Systems (LITS) Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Institute of Medical Information and Library Chinese Academy of Medical SciencesBeijingChina

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