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Knowledge graph relation prediction based on graph transformation

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Abstract

Knowledge graph relation prediction aims to predict the missing relation between entities. Many existing graph neural network (GNN)-based relation prediction models suffer from over-parameterization, and some models cannot effectively learn the correlation between relations for the relation prediction task. In order to solve the above problems, we propose a knowledge graph relation prediction model based on graph transformation. We use two kinds of graph transformation and a parallel fusion model to learn the semantic information, which effectively reduces the number of parameters and reduces the loss of semantic information compared to the Levi graph. Then, we utilize the self-attention mechanism to learn the correlation between relations, and combine it with the DistMult scoring function to complete the relation prediction task. Experiments on four real-world datasets WN18RR, CoDEx-S, Kinship, and FB15K-237 show that our model achieved a better balance between the number of parameters and prediction performance compared to existing GNN-based models on most datasets.

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Data will be made available on reasonable request.

References

  1. Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2019) Exploring high-order user preference on the knowledge graph for recommender systems. ACM Trans Inf Syst 37(3):1–26. https://doi.org/10.1145/3312738

    Article  MATH  Google Scholar 

  2. Cao Y, Wang X, He X, Hu Z, Chua T-S (2019) Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In: The World Wide Web Conference. WWW ’19. Association for Computing Machinery, New York, NY, USA, pp 151–161. doi:10.1145/3308558.3313705

  3. Wang H, Zhang F, Zhao M, Li W, Xie X, Guo M (2019) Multi-task feature learning for knowledge graph enhanced recommendation. WWW ’19. Association for Computing Machinery, New York, NY, USA, pp 2000–2010. doi:10.1145/3308558.3313411

  4. Hao Y, Liu H, He S, Liu K, Zhao J (2018) Pattern-revising enhanced simple question answering over knowledge bases. In: Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, New Mexico, USA, pp 3272–3282. https://aclanthology.org/C18-1277

  5. Long X, Zhao R, Sun J, Ju S (2023) Multi-view semantic reasoning networks for multi-hop question answering. Adv Eng Sci 55(2):285–297. https://doi.org/10.15961/j.jsuese.202200114

    Article  MATH  Google Scholar 

  6. Zhou H, Young T, Huang M, Zhao H, Xu J, Zhu X (2018) Commonsense knowledge aware conversation generation with graph attention. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. IJCAI’18. AAAI Press, Stockholm, Sweden, pp 4623–4629

  7. Schlichtkrull M, Kipf TN, Bloem P, Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings. Springer, Berlin, Heidelberg, pp. 593–607. doi:10.1007/978-3-319-93417-4_38

  8. Shikhar V, Soumya S, Vikram N, Partha T (2020) Composition-based multi-relational graph convolutional networks. In: International Conference on Learning Representations. pp 3061–3076. https://openreview.net/forum?id=BylA_C4tPr

  9. Zhu D (2024) Prgnn: Modeling high-order proximity with relational graph neural network for knowledge graph completion. Neurocomputing 594:127857. https://doi.org/10.1016/j.neucom.2024.127857

    Article  Google Scholar 

  10. Fang Y, Lu W, Liu X, Pedrycz W, Lang Q, Yang J (2023) Circulare: A complex space circular correlation relational model for link prediction in knowledge graph embedding. IEEE/ACM Trans. Audio Speech Lang Process 31:3162–3175. https://doi.org/10.1109/TASLP.2023.3297959

    Article  MATH  Google Scholar 

  11. Li X, Bo N, Li G, Jie W (2023) Relation-attention semantic-correlative knowledge graph embedding for inductive link prediction. Int J Mach Learn Cybern 14:3799–3811. https://doi.org/10.1007/s13042-023-01865-y

    Article  MATH  Google Scholar 

  12. Zhuo J, Zhu Q, Yue Y, Zhao Y, Han W (2022) A neighborhood-attention fine-grained entity typing for knowledge graph completion. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. WSDM ’22. Association for Computing Machinery, New York, NY, USA, pp 1525–1533. doi:10.1145/3488560.3498395

  13. Li W, Phyu S, Liu Q, Du B, Zhang J, Zhu H (2024) RSTIE-KGC: A relation sensitive textual information enhanced knowledge graph completion model. In: Shen W, Barthès JA, Luo J, Qiu T, Zhou X, Zhang J, Zhu H, Peng K, Xu T, Chen N (eds.) 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024, Tianjin, China, May 8-10, 2024. IEEE, pp 2991–2998. doi:10.1109/CSCWD61410.2024.10580566

  14. Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13. Curran Associates Inc., Red Hook, NY, USA, pp 2787–2795

  15. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI’14, pp. 1112–1119. AAAI Press, Québec City, Québec, Canada

  16. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. AAAI’15. AAAI Press, Austin, Texas, pp 2181–2187

  17. Ji G, He S, Xu L, Liu K, Zhao J (2015) Knowledge graph embedding via dynamic mapping matrix. In: Zong C, Strube M (eds.) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Beijing, China, pp 687–696. doi:10.3115/v1/P15-1067

  18. Wang S, Fu K, Sun X, Zhang Z, Li S, Jin L (2021) Hierarchical-aware relation rotational knowledge graph embedding for link prediction. Neurocomput. 458(C):259–270. https://doi.org/10.1016/j.neucom.2021.05.093

    Article  MATH  Google Scholar 

  19. Nickel M, Tresp V, Kriegel H-P (2011) A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on International Conference on Machine Learning. ICML’11. Omnipress, Madison, WI, USA, pp 809–816

  20. Krompaß D, Nickel M, Tresp V (2014) Large-scale factorization of type-constrained multi-relational data. In: 2014 International Conference on Data Science and Advanced Analytics (DSAA). pp 18–24. doi:10.1109/DSAA.2014.7058046

  21. Krompaß D, Baier S, Tresp V (2015) Type-constrained representation learning in knowledge graphs. In: The Semantic Web - ISWC 2015: 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part I. Springer, Berlin, Heidelberg, pp. 640–655. doi:10.1007/978-3-319-25007-6_37

  22. Yang B, Yih W-T, He X, Gao J, Deng L (2014) Embedding entities and relations for learning and inference in knowledge bases. In: International Conference on Learning Representations. https://api.semanticscholar.org/CorpusID:2768038

  23. Trouillon T, Welbl J, Riedel S, Gaussier E, Bouchard G (2016) Complex embeddings for simple link prediction. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning- Volume 48. ICML’16. JMLR.org, New York, NY, USA, pp 2071–2080

  24. Jagvaral B, Lee W-K, Roh J-S, Kim M-S, Park Y-T (2020) Path-based reasoning approach for knowledge graph completion using cnn-bilstm with attention mechanism. Expert Syst Appl 142:112960. https://doi.org/10.1016/j.eswa.2019.112960

    Article  Google Scholar 

  25. Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI’18/IAAI’18/EAAI’18. AAAI Press, New Orleans, Louisiana, USA, pp 1811–1818

  26. Nguyen DQ, Nguyen TD, Nguyen DQ, Phung D (2018) A novel embedding model for knowledge base completion based on convolutional neural network. In: Walker M, Ji H, Stent A (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Association for Computational Linguistics, New Orleans, Louisiana, pp 327–333. doi:10.18653/v1/N18-2053. https://aclanthology.org/N18-2053

  27. Cai L, Yan B, Mai G, Janowicz K, Zhu R (2019) Transgcn: Coupling transformation assumptions with graph convolutional networks for link prediction. In: Proceedings of the 10th International Conference on Knowledge Capture. K-CAP ’19. Association for Computing Machinery, New York, NY, USA, pp. 131–138. doi:10.1145/3360901.3364441

  28. Zeb A, Saif S, Chen J, Haq AU, Gong Z, Zhang D (2022) Complex graph convolutional network for link prediction in knowledge graphs. Expert Syst Appl 200:116796. https://doi.org/10.1016/j.eswa.2022.116796

    Article  Google Scholar 

  29. Li W, Peng R, Li Z (2022) Improving knowledge graph completion via increasing embedding interactions. Appl Intell 52(8):9289–9307. https://doi.org/10.1007/s10489-021-02947-6

    Article  MATH  Google Scholar 

  30. Zhang J, Huang J, Gao J, Han R, Zhou C (2022) Knowledge graph embedding by logical-default attention graph convolution neural network for link prediction. Inf Sci 593(C):201–215. https://doi.org/10.1016/j.ins.2022.01.076

    Article  MATH  Google Scholar 

  31. Yao S, Pi D, Chen J (2022) Knowledge embedding via hyperbolic skipped graph convolutional networks. Neurocomput 480(C):119–130. https://doi.org/10.1016/j.neucom.2022.01.037

    Article  MATH  Google Scholar 

  32. Nguyen DQ, Tong V, Phung D, Nguyen DQ (2022) Node co-occurrence based graph neural networks for knowledge graph link prediction. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. WSDM ’22. Association for Computing Machinery, New York, NY, USA, pp. 1589–1592. doi:10.1145/3488560.3502183

  33. Toutanova K, Chen D (2015) Observed versus latent features for knowledge base and text inference. In: Allauzen A, Grefenstette E, Hermann KM, Larochelle H, Yih SW-T (eds.) Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality. Association for Computational Linguistics, Beijing, China, pp. 57–66. doi:10.18653/v1/W15-4007 . https://aclanthology.org/W15-4007

  34. Beck D, Haffari G, Cohn T (2018) Graph-to-sequence learning using gated graph neural networks. In: Gurevych I, Miyao Y (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, pp. 273–283. doi:10.18653/v1/P18-1026. https://aclanthology.org/P18-1026

  35. Guo Z, Zhang Y, Teng Z, Lu W (2019) Densely connected graph convolutional networks for graph-to-sequence learning. Trans Assoc Comput Ling 7:297–312. https://doi.org/10.1162/tacl_a_00269

    Article  MATH  Google Scholar 

  36. Koncel-Kedziorski R, Bekal D, Luan Y, Lapata M, Hajishirzi H (2019) Text generation from knowledge graphs with graph transformers. In: Burstein J, Doran C, Solorio T (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, pp 2284–2293. doi:10.18653/v1/N19-1238 . https://aclanthology.org/N19-1238

  37. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17. Curran Associates Inc., Red Hook, NY, USA, pp 6000–6010

  38. Rahutomo F, Kitasuka T, Aritsugi M et al (2012) Semantic cosine similarity. Univ Seoul South Korea 4(1):1

    Google Scholar 

  39. Cohen I, Huang Y, Chen J, Benesty J, Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. Noise Reduc Speech Process 1–4

  40. Safavi T, Koutra D (2020) Codex: A comprehensive knowledge graph completion benchmark. In: Webber B, Cohn T, He Y, Liu Y (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp. 8328–8350. Online. doi:10.18653/v1/2020.emnlp-main.669. https://aclanthology.org/2020.emnlp-main.669

  41. Li Q, Wang D, Feng S, Niu C, Zhang Y (2022) Global graph attention embedding network for relation prediction in knowledge graphs. IEEE Trans Neural Netw Learn Syst 33(11):6712–6725. https://doi.org/10.1109/TNNLS.2021.3083259

    Article  MathSciNet  MATH  Google Scholar 

  42. Kingma D, Ba J (2014) Adam: A method for stochastic optimization. Comput Sci

  43. Maaten L, Hinton GE (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  44. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. CoRR. arXiv:1609.02907

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Funding

This paper is supported by the National Natural Science Foundation of China, grant numbers 62062050 and 62362052, and the Innovation Foundation for Postgraduate Student of Jiangxi Province, grant number YC2023-S694.

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Conceptualization: Hongjian Zhao, Linlan Liu, Jian Shu, Weide Huang; Methodology: Weide Huang, Hongjian Zhao; Validation, formal analysis, and investigation: Linlan Liu, Weide Huang, Jian Shu, Hongjian Zhao; Article original draft preparation: Hongjian Zhao; Article review and editing: Linlan Liu, Weide Huang, Jian Shu, Hongjian Zhao; Supervision: Linlan Liu, Jian Shu. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Weide Huang.

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The datasets used for this experiment are publicly available by the respective organizations/authors to further improve the knowledge graph research field. Thus, informed consent is not required to use the dataset. References and citations to relevant datasets are included in the manuscript.

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Liu, L., Huang, W., Shu, J. et al. Knowledge graph relation prediction based on graph transformation. Appl Intell 55, 241 (2025). https://doi.org/10.1007/s10489-024-06080-y

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