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Stepwise relation prediction with dynamic reasoning network for multi-hop knowledge graph question answering

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Abstract

Multi-hop knowledge graph question answering (KGQA) targets at pinpointing the answer entities to a natural language question by reasoning across multiple triples in knowledge graphs (KGs). When faced with multi-hop questions, existing methods take the whole relation paths into consideration, whereas the number of candidate paths grows exponentially with the increasement of path length, resulting in high search space complexity. Meanwhile, due to the complex semantic information, it is important to focus on different parts of the question at different steps. Moreover, previous studies only give the predicted answers but lack a relational path to indicate the reasoning process. To address these challenges, this paper proposes an interpretable neural model for multi-hop KGQA, namely Dynamic Reasoning Network (DRN). Inspired by human’s hop-by-hop reasoning behavior, DRN employs an interpretable, stepwise reasoning process to predict a relation at each step, all the intermediate relations form a traceable reasoning path. With effectively stepwise path search over KGs, DRN reduces the search space significantly. Furthermore, to facilitate semantic parsing, DRN dynamically updates the representation of relations and questions for each step based on attention mechanism. Extensive experiments conducted over four benchmark datasets from football, movie and general domain well demonstrate the effectiveness of our method.

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Data Availability

All the experimental datasets in this paper are public. In addition, the literature or website sources of the experimental data have been provided in the form of references or footnotes.

Code Availability

Code and deployment instructions are available upon request by the authors.

Notes

  1. https://github.com/thunlp/OpenKE

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Funding

This work is supported by the National Natural Science Foundation of China under grant No.61872163 and 61806084, Jilin Province Key Scientific and Technological Research and Development Project under grant No.20210201131GX, and Jilin Provincial Education Department project under grant No.JJKH20190160KJ.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China

    Hai Cui, Tao Peng, Tie Bao, Ridong Han & Lu Liu

  2. College of Software, Jilin University, Changchun, 130012, Jilin, China

    Tao Peng, Tie Bao & Lu Liu

  3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China

    Tao Peng, Tie Bao & Lu Liu

  4. Department of Linguistics, University of Washington, Seattle, 98195, WA, United States

    Jiayu Han

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  1. Hai Cui
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  2. Tao Peng
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  3. Tie Bao
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  4. Ridong Han
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Correspondence to Tao Peng or Lu Liu.

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Cui, H., Peng, T., Bao, T. et al. Stepwise relation prediction with dynamic reasoning network for multi-hop knowledge graph question answering. Appl Intell 53, 12340–12354 (2023). https://doi.org/10.1007/s10489-022-04127-6

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