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Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search

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Abstract

Natural language question answering over knowledge graph has received widespread attention. However, the existing methods always aim to improve every phase of natural language question answering and neglect the defects; namely, not all query intentions can be identified and mapped to the correct SPARQL statement. In contrast, keyword search relies on the links among multiple keywords regardless of the exact logic relations in question. Therefore, we propose a framework (abbreviated as NLQSK for title of this paper) that introduces keyword search into natural language question answering to compensate for the defects mentioned above. First, we translate a natural language question into top-k SPARQL statements by using the existing methods. Second, we transform the valuable information that cannot be identified and mapped into keywords, and then, return the neighboring information in a knowledge graph by keyword index. Third, we combine the SPARQL block (i.e., the SPARQL statement and its result) and keyword search to produce the answer to the natural language question. Finally, the experiments on the benchmark dataset confirm that keyword search can compensate for the defects of natural language question answering and that NLQSK can answer more questions than the existing state-of-the-art question answering systems.

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Acknowledgements

This work was supported by the youth Project of science and technology research program of Chongqing Education Commission of China (No. KJQN201901414 and No. KJQN201901408), the Startup Foundation for Introducing Talent of Yangtze Normal University (No. 0107/011160052), the PhD Candidate Talent Development Project (No. BYJS201908), the Project of Chongqing Natural Science Foundation (No. cstc2019jcyj-msxmX0683 and No. cstc2019jcyj-msxm1579), major Project of science and technology research program of Chongqing Education Commission of China (No. KJZD-M201901401), the National Natural Science Foundation of China (Grant No. 61672102 and No. 61802244), the Program for New Century Excellent Talents in University of Ministry of Education of China (Grant No. NCET-10–0239) and the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2019JQ-668).

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Authors and Affiliations

  1. College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing, China

    Xin Hu & Jiangli Duan

  2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China

    Jiangli Duan

  3. School of Artificial Intelligence, Beijing Normal University, Beijing, China

    Depeng Dang

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  1. Xin Hu
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  2. Jiangli Duan
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  3. Depeng Dang
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Correspondence to Jiangli Duan.

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Hu, X., Duan, J. & Dang, D. Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search. Knowl Inf Syst 63, 819–844 (2021). https://doi.org/10.1007/s10115-020-01534-4

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