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Subgraph retrieval and link scoring model for multi-hop question answering in knowledge graphs

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Abstract

Knowledge graphs (KGs) is an associated network composed of semantic relationships. The goal of the knowledge graph question answering (KGQA) is to provide answers to natural language questions based on KGs. Multi-hop KGQA requires reasoning on multiple edges of KGs to get the correct answer. However, KGs are usually incomplete, with numerous missing relationships in reality, which brings challenges to KGQA, especially for multi-hop KGQA. In this work, we propose an efficient approach for multi-hop KGQA. To capture more comprehensive features on incomplete KGs, we utilize Tucker Entity Relation (TuckER) decomposition for link prediction on the binary tensor representation of KGs and train a knowledge graph embedding (KGE) model and apply the learned representation for downstream QA tasks. We employ a pre-trained language model to assess the relevance scoring of questions and each node after subgraph retrieval. Additionally, we introduce a link scoring strategy based on the triple scoring function to address the limitations of solely relying on KGE for answer scoring. Through extensive experiments conducted on multiple benchmark datasets, we demonstrate the effectiveness of our proposed model in facilitating multi-hop QA reasoning on incomplete KGs.

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Availability of Data and Materials

All data generated or analysed during this study are included in these published articles [30,31,32, 34, 36, 40]

Code Availability

The code used in the experiment can be obtained from the first author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 61972059) and the China Postdoctoral Science Foundation (No. 2021M692368).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, No. 1 Shizi Street, Suzhou, 215000, Jiangsu, China

    Changshun Zhou & Shengrong Gong

  2. School of Computer Science and Engineering, Changshu Institute of Technology, No. 99 Hushan Road, Suzhou, 215000, Jiangsu, China

    Wenhao Ying, Shan Zhong & Shengrong Gong

  3. School of Information, Liaoning University, No. 66 Chongshan Middle Road, Shenyang, 110036, Liaoning, China

    Han Yan

Authors
  1. Changshun Zhou
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  2. Wenhao Ying
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  3. Shan Zhong
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  4. Shengrong Gong
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  5. Han Yan
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Contributions

Changshun Zhou: Collecting data, building models, conducting experiments, and analyzing experimental data; writing and revising the paper. Wenhao Ying: Proposing and determining the research direction, designing research plans. Shan Zhong: Conducting benchmark comparison experiments. Shengrong Gong: Revising the paper and final version editing. Han Yan: Conducting ablative experiments.

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Correspondence to Wenhao Ying.

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The data used in this paper are all from public datasets, and informed consent was obtained from all authors for the data used in this paper.

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Zhou, C., Ying, W., Zhong, S. et al. Subgraph retrieval and link scoring model for multi-hop question answering in knowledge graphs. Appl Intell 55, 431 (2025). https://doi.org/10.1007/s10489-024-05935-8

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