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A knowledge inference model for question answering on an incomplete knowledge graph

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Abstract

Question answering over knowledge graph (KGQA) is a task to solve natural language questions on knowledge graphs (KGs). Multi-hop KGQA requires multi-steps reasoning on the KG to find the correct answers to complex questions. However, it is difficult to find the triple required by the question directly when solving complex multi-hop questions for KGs with missing links. To mitigate this challenge, we propose an effective reasoning model that fuses neighbor interaction and a relation recognition module for multi-hop QA. Specifically, we adopt neighbor interaction networks to learn a better entity representation. The model identifies the relations contained in the questions through neural networks to further precisely determine the range of answers. Our method selectively captures the complex hidden information within the KG and overcomes the limitation of the answer range. It can perform well without the help of additional text corpora. The experimental results on two datasets show that our model can effectively capture richer semantic information for reasoning and achieve better results than all baseline models.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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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 2019JZZY010129, 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 Engineering, Shandong Normal University, Jinan, 250357, China

    Qimeng Guo, Xue Wang, Peiyu Liu & Liancheng Xu

  2. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China

    Zhenfang Zhu

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  1. Qimeng Guo
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  2. Xue Wang
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Correspondence to Peiyu Liu or Liancheng Xu.

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Guo, Q., Wang, X., Zhu, Z. et al. A knowledge inference model for question answering on an incomplete knowledge graph. Appl Intell 53, 7634–7646 (2023). https://doi.org/10.1007/s10489-022-03927-0

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