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RuKBC-QA: A Framework for Question Answering over Incomplete KBs Enhanced with Rules Injection

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 12431))

Abstract

The incompleteness of the knowledge base (KB) is one of the key issues when answering natural language questions over an incomplete knowledge base (KB-QA). To alleviate this problem, a framework, RuKBC-QA, is proposed to integrate methods of rule-based knowledge base completion (KBC) into general QA systems. Three main components are included in our framework, namely, a rule miner that mines logic rules from the KB, a rule selector that selects meaningful rules for QA, and a QA model that aggregates information from the original knowledge base and the selected rules. Experiments on WEBQUESTIONS dataset indicate that the proposed framework can effectively alleviate issues caused by incompleteness and obtains a significant improvement in terms of micro average Fl score by 2.4% to 4.5% under different incompleteness settings.

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Acknowledgement

This work is supported by National Key Research and Development Program of China under grant 2016YFB1000902; And NSFC Project No. 61472412 and No. 61621003.

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Authors and Affiliations

  1. Academy of Mathematics and Systems Science and Key Lab-MADIS, Chinese Academy of Sciences, Beijing, 100190, China

    Qilin Sun

  2. School of the Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Qilin Sun

  3. School of Modern Posts and Institute of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Weizhuo Li

Authors
  1. Qilin Sun

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  2. Weizhuo Li

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Corresponding author

Correspondence toQilin Sun.

Editor information

Editors and Affiliations

  1. ECE & Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, Canada

    Xiaodan Zhu

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Min Zhang

  3. School of Computer Science and Technology, Soochow University, Suzhou, China

    Yu Hong

  4. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Ruifang He

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Sun, Q., Li, W. (2020). RuKBC-QA: A Framework for Question Answering over Incomplete KBs Enhanced with Rules Injection. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_7

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