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ReLMKG: reasoning with pre-trained language models and knowledge graphs for complex question answering

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Abstract

The goal of complex question answering over knowledge bases (KBQA) is to find an answer entity in a knowledge graph. Recent information retrieval-based methods have focused on the topology of the knowledge graph, ignoring inconsistencies between knowledge graph embeddings and natural language embeddings, and cannot effectively utilize both implicit and explicit knowledge for reasoning. In this paper, we propose a novel model, ReLMKG, to address this challenge. This approach performs joint reasoning on a pre-trained language model and the associated knowledge graph. The complex question and textual paths are encoded by the language model, bridging the gap between the question and the knowledge graph and exploiting implicit knowledge without introducing additional unstructured text. The outputs of different layers in the language model are used as instructions to guide a graph neural network to perform message propagation and aggregation in a step-by-step manner, which utilizes the explicit knowledge contained in the structured knowledge graph. We analyse the reasoning ability of the ReLMKG model for knowledge graphs with different degrees of sparseness and evaluate the generalizability of the model. Experiments conducted on the Complex WebQuestions and WebQuestionsSP datasets demonstrate the effectiveness of our approach on KBQA tasks.

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Data Availability

The data generated and analysed during the current study are available from the corresponding author upon reasonable request.

Code Availability

Some or all models, code that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. https://huggingface.co/bert-base-uncased

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Acknowledgments

This research was supported by the Fundamental Research Funds for the Central Universities (Grant number 2020YJS012).

Funding

This research was funded by the Fundamental Research Funds for the Central Universities (Grant number 2020YJS012).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Xing Cao & Yun Liu

  2. Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing, 100044, China

    Xing Cao & Yun Liu

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  1. Xing Cao
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  2. Yun Liu
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Xing Cao and Yun Liu designed the study and performed the experiments; Xing Cao performed the experiments, analyzed the data, and wrote the manuscript.

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Correspondence to Yun Liu.

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Cao, X., Liu, Y. ReLMKG: reasoning with pre-trained language models and knowledge graphs for complex question answering. Appl Intell 53, 12032–12046 (2023). https://doi.org/10.1007/s10489-022-04123-w

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