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A reasoning enhance network for muti-relation question answering

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Abstract

Multi-relation Question Answering is an important task of knowledge base over question answering (KBQA), multi-relation means that the question contains multiple relations and entity information, so it needs to use the fact triples in the knowledge base to analyze and reasoning the question in more detail. In this paper, we propose a novel model called Reasoning Enhance Network that uses context information, enhance the accuracy of relation and entity predicted in each hop. The model obtains the relation by analyzing the context information before each hop start, and then reasons the answer by the previous information; update question representation and reasoning state through predicted relation and entity, then promote the next hop reasoning starts. Our experiments clearly show that our method achieves good results on four datasets. Also, since we use attention mechanisms, our method offers better interpretability.

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Acknowledgements

This work was supported in part by the National Social Science Foundation under Award 19BYY076, in part Key R & D project of Shandong Province 2019 JZZY010129, and in part by the Shandong Provincial Social Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award 18BJYJ04.

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Authors and Affiliations

  1. School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan, 250357, China

    Wenqing Wu, Zhenfang Zhu & Guangyuan Zhang

  2. Chinese Lexicography Research Center, Lu Dong University, Yantai, 264025, China

    Shiyong Kang

  3. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Peiyu Liu

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  1. Wenqing Wu
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  2. Zhenfang Zhu
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  3. Guangyuan Zhang
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Correspondence to Zhenfang Zhu or Guangyuan Zhang.

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Wu, W., Zhu, Z., Zhang, G. et al. A reasoning enhance network for muti-relation question answering. Appl Intell 51, 4515–4524 (2021). https://doi.org/10.1007/s10489-020-02111-6

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