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An improving reasoning network for complex question answering over temporal knowledge graphs

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Abstract

Question Answering Over Temporal Knowledge Graphs is an important topic in question answering, which aims to find an entity or timestamp to answer temporal reasoning questions from temporal knowledge graphs. Answering complex questions remains a major challenge for question answering over temporal knowledge graphs because it is associated with complex temporal reasoning. The performance of the existing state-of-the-art model falls short when the question contains constraints (e.g., ‘before/after’, ‘first/last’ and ‘during’) that require complex temporal reasoning based on multiple relevant facts. In this paper, we propose an improving reasoning method called the Complex Temporal Reasoning Network, which improves the complex temporal reasoning for temporal reasoning questions. For each question, we capture implicit temporal features and relation representation and then integrate them to generate implicit temporal relation representation. The experimental results on the CRONQUESTIONS dataset demonstrate that our method significantly outperforms all baselines. In particular, we demonstrate the effectiveness of our method on complex questions. The source code of CTRN will be available at https://github.com/2399240664/CTRN.

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Data Availability Statement

The data that support the findings of this study are openly available at https://github.com/apoorvumang/CronKGQA.

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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, in part Shandong Natural Science Foundation under Award ZR2021MF064 and Award ZR2021QG041, and in part by the Shandong Provincial Social Science Planning Project under Award 19BJCJ51.

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Authors and Affiliations

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

    Songlin Jiao, Zhenfang Zhu, Wenqing Wu, Zicheng Zuo, Jiangtao Qi & Guangyuan Zhang

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

    Wenling Wang

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

    Peiyu Liu

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  1. Songlin Jiao
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Correspondence to Zhenfang Zhu or Jiangtao Qi.

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Jiao, S., Zhu, Z., Wu, W. et al. An improving reasoning network for complex question answering over temporal knowledge graphs. Appl Intell 53, 8195–8208 (2023). https://doi.org/10.1007/s10489-022-03913-6

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