Skip to main content

Advertisement

Springer Nature Link
Log in

Simplified multi-view graph neural network for multilingual knowledge graph completion

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Knowledge graph completion (KGC) aims to fill in missing entities and relations within knowledge graphs (KGs) to address their incompleteness. Most existing KGC models suffer from knowledge coverage as they are designed to operate within a single KG. In contrast, Multilingual KGC (MKGC) leverages seed pairs from different language KGs to facilitate knowledge transfer and enhance the completion of the target KG. Previous studies on MKGC based on graph neural networks (GNNs) have primarily focused on using relation-aware GNNs to capture the combined features of neighboring entities and relations. However, these studies still have some shortcomings, particularly in the context of MKGCs. First, each language’s specific semantics, structures, and expressions contribute to the increased heterogeneity of the KG. Therefore, the completion of MKGCs necessitates a thorough consideration of the heterogeneity of the KG and the effective integration of its heterogeneous features. Second, MKGCs typically have a large graph scale due to the need to store and manage information from multiple languages. However, current relation-aware GNNs often inherit complex GNN operations, resulting in unnecessary complexity. Therefore, it is necessary to simplify GNN operations. To address these limitations, we propose a Simplified Multi-view Graph Neural Network (SM-GNN) for MKGC. SM-GNN incorporates two simplified multi-view GNNs as components. One GNN is utilized for learning multi-view graph features to complete the KG. The other generates new alignment pairs, facilitating knowledge transfer between different views of the KG. We simplify the two multiview GNNs by retaining feature propagation while discarding linear transformation and nonlinear activation to reduce unnecessary complexity and effectively leverage graph contextual information. Extensive experiments demonstrate that our proposed model outperforms competing baselines. The code and dataset are available at the website of github.com/dbbice/SM-GNN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Yan J, Wang C, Cheng W, Gao M, Zhou A. A retrospective of knowledge graphs. Frontiers of Computer Science, 2018, 12(1): 55–74

    Article  Google Scholar 

  2. Ji S, Pan S, Cambria E, Marttinen P, Yu P S. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 494–514

    Article  MathSciNet  Google Scholar 

  3. Liu Y, Zhang K, Huang Z, Wang K, Zhang Y, Liu Q, Chen E. Enhancing hierarchical text classification through knowledge graph integration. In: Proceedings of the Association for Computational Linguistics. 2023, 5797–5810

    Google Scholar 

  4. Wu X, Jiang T, Zhu Y, Bu C. Knowledge graph for China’s genealogy. In: Proceedings of 2020 IEEE International Conference on Knowledge Graph. 2020, 529–535

    Google Scholar 

  5. Bu C, Zhang J, Yu X, Wu L, Wu X. Which companies are likely to invest: knowledge-graph-based recommendation for investment promotion. In: Proceedings of 2022 IEEE International Conference on Data Mining. 2022, 11–20

    Google Scholar 

  6. Lin Q, Mao R, Liu J, Xu F, Cambria E. Fusing topology contexts and logical rules in language models for knowledge graph completion. Information Fusion, 2023, 90: 253–264

    Article  Google Scholar 

  7. Xu D, Xu T, Wu S, Zhou J, Chen E. Relation-enhanced negative sampling for multimodal knowledge graph completion. In: Proceedings of the 30th ACM International Conference on Multimedia. 2022, 3857–3866

    Chapter  Google Scholar 

  8. Liu F, Shen Y, Zhang T, Gao H. Entity-related paths modeling for knowledge base completion. Frontiers of Computer Science, 2020, 14(5): 145311

    Article  Google Scholar 

  9. Choudhary S, Luthra T, Mittal A, Singh R. A survey of knowledge graph embedding and their applications. 2021, arXiv preprint arXiv: 2107.07842

  10. Rossi A, Barbosa D, Firmani D, Matinata A, Merialdo P. Knowledge graph embedding for link prediction: a comparative analysis. ACM Transactions on Knowledge Discovery from Data, 2021, 15(2): 14

    Article  Google Scholar 

  11. Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 2787–2795

    Google Scholar 

  12. Sun Z, Deng Z-H, Nie J-Y, Tang J. RotatE: knowledge graph embedding by relational rotation in complex space. In: Proceedings of the 7th International Conference on Learning Representations. 2019

    Google Scholar 

  13. Li R, Cao Y, Zhu Q, Bi G, Fang F, Liu Y, Li Q. How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence. 2022, 5781–5791

    Google Scholar 

  14. Wang Q, Mao Z, Wang B, Guo L. Knowledge graph embedding: a survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724–2743

    Article  Google Scholar 

  15. Chen X, Chen M, Fan C, Uppunda A, Sun Y, Zaniolo C. Multilingual knowledge graph completion via ensemble knowledge transfer. In: Proceedings of the Association for Computational Linguistics. 2020, 3227–3238

    Google Scholar 

  16. Huang Z, Li Z, Jiang H, Cao T, Lu H, Yin B, Subbian K, Sun Y, Wang W. Multilingual knowledge graph completion with self-supervised adaptive graph alignment. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 2022, 474–485

    Google Scholar 

  17. Tong V, Nguyen D Q, Huynh T T, Nguyen T T, Nguyen Q V H, Niepert M. Joint multilingual knowledge graph completion and alignment. In: Proceedings of the Association for Computational Linguistics. 2022, 4646–4658

    Google Scholar 

  18. Li Z, Zhang Q, Zhu F, Li D, Zheng C, Zhang Y. Knowledge graph representation learning with simplifying hierarchical feature propagation. Information Processing & Management, 2023, 60(4): 103348

    Article  Google Scholar 

  19. Dettmers T, Minervini P, Stenetorp P, Riedel S. Convolutional 2D knowledge graph embeddings. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018, 1811–1818

    Google Scholar 

  20. Yao Z, Zhang W, Chen M, Huang Y, Yang Y, Chen H. Analogical inference enhanced knowledge graph embedding. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 4801–4808

    Google Scholar 

  21. Le T, Le N, Le B. Knowledge graph embedding by relational rotation and complex convolution for link prediction. Expert Systems with Applications, 2023, 214: 119122

    Article  Google Scholar 

  22. Fanourakis N, Efthymiou V, Kotzinos D, Christophides V. Knowledge graph embedding methods for entity alignment: experimental review. Data Mining and Knowledge Discovery, 2023, 37(5): 2070–2137

    Article  MathSciNet  Google Scholar 

  23. Liu C, Li L, Yao X, Tang L. A survey of recommendation algorithms based on knowledge graph embedding. In: Proceedings of the 2019 IEEE International Conference on Computer Science and Educational Informatization. 2019, 168–171

    Google Scholar 

  24. Shokrzadeh Z, Feizi-Derakhshi M R, Balafar M A, Mohasefi J B. Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding. Ain Shams Engineering Journal, 2024, 15(1): 102263

    Article  Google Scholar 

  25. Wang Z, Zhang J, Feng J, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 1112–1119

    Google Scholar 

  26. Lin Y, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2181–2187

    Google Scholar 

  27. Yao L, Mao C S, Luo Y. KG-BERT: Bert for knowledge graph completion. 2019, arXiv preprint arXiv:1909.03193

  28. Yao L, Mao C, Luo Y. Graph convolutional networks for text classification. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 7370–7377

    Google Scholar 

  29. He X, Deng K, Wang X, Li Y, Zhang Y D, Wang M. LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 639–648

    Chapter  Google Scholar 

  30. Mao K L, Zhu J M, Xiao X, Lu B, Wang Z W, He X Q. UltraGCN: Ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 1253–1262

    Chapter  Google Scholar 

  31. Schlichtkrull M, Kipf T N, Bloem P, Van Den Berg R, Titov I, Welling M. Modeling relational data with graph convolutional networks. In: Proceedings of the 15th International Conference on the Semantic Web. 2018, 593–607

    Google Scholar 

  32. Vashishth S, Sanyal S, Nitin V, Talukdar P P. Composition-based multirelational graph convolutional networks. In: Proceedings of the 8th International Conference on Learning Representations. 2020

    Google Scholar 

  33. Wu F, Souza A H Jr, Zhang T, Fifty C, Yu T, Weinberger K Q. Simplifying graph convolutional networks. In: Proceedings of the 36th International Conference on Machine Learning. 2019, 6861–6871

    Google Scholar 

  34. Wang H, Dai S, Su W, Zhong H, Fang Z, Huang Z, Feng S, Chen Z, Sun Y, Yu D. Simple and effective relation-based embedding propagation for knowledge representation learning. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence. 2022, 2755–2761

    Google Scholar 

  35. Jiang T, Bu C, Zhu Y, Wu X. Integrating symbol similarities with knowledge graph embedding for entity alignment: an unsupervised framework. Intelligent Computing, 2023, 2: 0021

    Article  Google Scholar 

  36. Jiang T T, Bu C Y, Zhu Y, Wu X D. Combining embedding-based and symbol-based methods for entity alignment. Pattern Recognition, 2022, 124: 108433

    Article  Google Scholar 

  37. Jiang T T, Bu C Y, Zhu Y, Wu X D. Two-stage entity alignment: combining hybrid knowledge graph embedding with similarity-based relation alignment. In: Proceedings of the 16th Pacific Rim International Conference on Artificial Intelligence. 2019, 162–175

    Google Scholar 

  38. Zhu H, Xie R, Liu Z, Sun M. Iterative entity alignment via joint knowledge embeddings. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 4258–4264

    Google Scholar 

  39. Zhu J, Huang C, De Meo P. DFMKE: a dual fusion multi-modal knowledge graph embedding framework for entity alignment. Information Fusion, 2023, 90: 111–119

    Article  Google Scholar 

  40. Chen M, Tian Y, Yang M, Zaniolo C. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 1511–1517

    Google Scholar 

  41. Sun Z, Hu W, Zhang Q, Qu Y. Bootstrapping entity alignment with knowledge graph embedding. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018, 4396–4402

    Google Scholar 

  42. Wang Z, Lv Q, Lan X, Zhang Y. Cross-lingual knowledge graph alignment via graph convolutional networks. In: Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 349–357

    Chapter  Google Scholar 

  43. Singh H, Chakrabarti S, Jain P, Choudhury S R, Mausam. Multilingual knowledge graph completion with joint relation and entity alignment. In: Proceedings of the 3rd Conference on Automated Knowledge Base Construction. 2021

    Google Scholar 

  44. Akhtar M U, Liu J, Xie Z, Liu X, Ahmed S, Huang B. Entity alignment based on relational semantics augmentation for multilingual knowledge graphs. Knowledge-Based Systems, 2022, 252: 109494

    Article  Google Scholar 

  45. Wu Y, Liu X, Feng Y, Wang Z, Yan R, Zhao D. Relation-aware entity alignment for heterogeneous knowledge graphs. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 5278–5284

    Google Scholar 

  46. Yang B, Yih W T, He X, Gao J, Deng L. Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd International Conference on Learning Representations. 2015

    Google Scholar 

  47. Devlin J, Chang M-W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019, 4171–4186

    Google Scholar 

  48. Demsar J. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 2006, 7: 1–30.

    MathSciNet  Google Scholar 

  49. Mao X, Wang W, Wu Y, Lan M. Are negative samples necessary in entity alignment?: an approach with high performance, scalability and robustness. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 1263–1273

    Chapter  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62120106008, 61976077, 61806065, 62076085, and 91746209), and the Fundamental Research Funds for the Central Universities (JZ2022HGTB0239).

Author information

Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, 230009, China

    Bingbing Dong, Chenyang Bu, Yi Zhu & Xindong Wu

  2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Bingbing Dong, Chenyang Bu, Yi Zhu & Xindong Wu

  3. School of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Yi Zhu

  4. School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230000, China

    Shengwei Ji

Authors
  1. Bingbing Dong
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chenyang Bu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yi Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shengwei Ji
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xindong Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xindong Wu.

Ethics declarations

Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

Additional information

Bingbing Dong is a doctoral student in the Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) and the School of Computer Science and Information Engineering, Hefei University of Technology, China. She obtained her ME degree from the Hefei University of Technology, China in 2021. Her main research interests include data mining, knowledge engineering, and knowledge reasoning.

Chenyang Bu is an associate professor at Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China. He obtained the PhD degree from University of Science and Technology of China. His research interests include knowledge graph construction and application, as well as automated graph learning with evolutionary algorithms.

Yi Zhu is currently an assistant professor in the School of Information Engineering, Yangzhou University, China. He received the BS degree from Anhui University, and the MS degree from University of Science and Technology of China, and the PhD degree from Hefei University of Technology, China. His research interests are in data mining and knowledge engineering. His research interests include data mining and recommendation systems.

Shengwei Ji is currently an assistant professor with Hefei University, China. He received his PhD degree from Hefei University of Technology, China in 2022, and BS degree from Changan University, China in 2016. He has published several peer-reviewed papers in prestigious journals and top international conferences including ACM TIST, IEEE TETC, IEEE ICDCS, and PRICAI. His research fields mainly lie in local graph learning, distributed computing, and knowledge graph.

Xindong Wu received bachelor’s and master’s degrees in computer science from the Hefei University of Technology, China in 1984 and 1987, respectively, and a PhD degree in artificial intelligence from the University of Edinburgh, UK in 1993. He is the Director and Professor of the Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China, and a Senior Research Specialist at the Research Center for Knowledge Engineering at Zhejiang Lab, China. His research interests include big data analytics, data mining, and knowledge engineering. He is a Foreign Member of the Russian Academy of Engineering and a Fellow of the AAAS (American Association for the Advancement of Science) and the IEEE. He is the Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM) and the Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE) between 2005 and 2008 and Co-Editor-in-Chief of the ACM Transactions on Knowledge Discovery from Data Engineering between 2017 and 2020. He served as a program committee chair/cochair for ICDM 2003 (the 3rd IEEE International Conference on Data Mining), KDD 2007 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and ICBK 2017 (the 8th IEEE International Conference on Big Knowledge).

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, B., Bu, C., Zhu, Y. et al. Simplified multi-view graph neural network for multilingual knowledge graph completion. Front. Comput. Sci. 19, 197324 (2025). https://doi.org/10.1007/s11704-024-3577-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-024-3577-3

Keywords