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Causal Inference-Based Debiasing Framework for Knowledge Graph Completion

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The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14265))

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Abstract

The task of Knowledge Graph Completion (KGC) entails inferring missing relations and facts in a partially specified graph to discover new knowledge. However, the discrepancy in the targets between the training and inference phases might lead to in-depth bias and in-breadth bias during inference, potentially resulting in incorrect outcomes. In this work, we conduct a comprehensive analysis of these biases to determine their extent of impact. To mitigate these biases, we propose a novel debiasing framework called Causal Inference-based Debiasing Framework for KGC (CIDF) by formulating a causal graph and utilizing it for causal analysis of KGC tasks. The framework incorporates In-Depth Bias Mitigation to diminish the bias on feature representations by measuring the bias during inference, and In-Breadth Bias Mitigation to increase the distinguishability between feature representations by introducing a novel loss function. We evaluate the effectiveness of our proposed method on four benchmark datasets - WN18RR, FB15k-237, Wikidata5M-Trans, and Wikidata5M-Ind, achieving improvements of 2.5%, 0.9%, 3.2%, and 1.5% on Hit@1 respectively. Our results demonstrate that CIDF leads to significant improvements on these datasets, with more substantial gains observed in the biased settings on WN18RR achieving a 3.4% improvement in Hit@1.

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Notes

  1. 1.

    https://deepgraphlearning.github.io/project/wikidata5m.

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Acknowledgements

National Natural Science Foundation of China, Grant/Award Number: 61402220; The Philosophy and Social Science Foundation of Hunan Province, Grant/Award Number: 16YBA323; Natural Science Foundation of Hunan Province, Grant/Award Number: 2020JJ4525,2022JJ30495; Scientific Research Fund of Hunan Provincial Education Department, Grant/Award Number: 18B279,19A439,22A0316.

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  1. University of South China, Hengyang, China

    Lin Ren, Yongbin Liu & Chunping Ouyang

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  1. Lin Ren
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  2. Yongbin Liu
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  3. Chunping Ouyang
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Correspondence to Yongbin Liu .

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Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, Beijing, China

    Juanzi Li

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Ren, L., Liu, Y., Ouyang, C. (2023). Causal Inference-Based Debiasing Framework for Knowledge Graph Completion. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14265. Springer, Cham. https://doi.org/10.1007/978-3-031-47240-4_18

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