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Knowledge graph embedding and completion based on entity community and local importance

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Abstract

Knowledge graph completion can solve the common problems of missing and incomplete knowledge in the process of building knowledge graphs by predicting the missing entity and relationship information in the knowledge base. To the best of our knowledge, existing knowledge graph completion algorithms seldom consider the influence of entity communities, and no algorithm further considers the influence of local importance based on entity communities. In this paper, we propose a knowledge graph embedding model and completion method based on entity feature information. First, we use the community detection method to divide the knowledge graph into different entity communities, and calculate the local importance of the entity in the community. Next, we apply community information to obtain entities and relationships with low similarities to construct more appropriate negative triples. A new hybrid objective function that can simultaneously reflect the importance of entities and the structure of the knowledge graph is proposed to obtain high-quality entity and relationship embedding vectors to complete the knowledge graph. On the FreeBase and WordNet datasets, through comparison with six well-known knowledge graph completion methods, the experimental results show that our proposed algorithm has good completion performance.

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References

  1. Yang M, Chen L, Lyu Z, Liu J, Shen Y, Wu Q (2020) Hierarchical fusion of common sense knowledge and classifier decisions for answer selection in community question answering. Neural Networks 132:53–65

    Article  Google Scholar 

  2. Yang Y, Zhu Y, Li Y (2022) Personalized recommendation with knowledge graph via dual-autoencoder. Appl Intell 52(6):6196–6207

  3. Vo AD, Nguyen QP, Ock CY (2020) Semantic and syntactic analysis in learning representation based on a sentiment analysis model. Appl intell 50(3):663–680

    Article  Google Scholar 

  4. Wang M, Wang H, Qi G, Zheng Q (2020) Richpedia: A large-scale, comprehensive multi-modal knowledge graph. Big Data Res 22:100159

    Article  Google Scholar 

  5. Ji S, Pan S, Cambria E, Marttinen P, Philip SY (2021) A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems 33(2):494–514

    Article  MathSciNet  Google Scholar 

  6. Feng J, Wei Q, Cui J, Chen J (2021) Novel translation knowledge graph completion model based on 2d convolution. Appl Intell, 1–10

  7. Jenatton R, Le Roux N, Bordes A, Obozinski G (2012) A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), p 3176–3184

  8. Yang B, Yih SW t, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR)

  9. Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G (2016) Complex embeddings for simple link prediction. In: International Conference on Machine Learning, p 2071–2080

  10. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26

  11. Ebisu T, Ichise R (2019) Generalized translation-based embedding of knowledge graph. IEEE Transactions on Knowledge and Data Engineering 32(5):941–951

    Article  Google Scholar 

  12. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 28

  13. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI Conference on Artificial Intelligence

  14. Yu M, Zhang Q, Yu J, Zhao M, Li X, Jin D, Yang M, Yu R (2022) Knowledge graph completion using topological correlation and multi-perspective independence. Knowledge-Based Systems, 110031

  15. Perozzi B, Al-Rfou, R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p 701–710

  16. Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining, p 855–864

  17. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, p 1067–1077

  18. Guo G, Zhou H, Chen B, Liu Z, Xu X, Chen X, Dong Z, He X (2022) Ipgan: Generating informative item pairs by adversarial sampling. IEEE transactions on neural networks and learning systems 33(2):694–706

    Article  Google Scholar 

  19. Chen J, Zhong M, Li J, Wang D, Qian T, Tu H (2021) Effective deep attributed network representation learning with topology adapted smoothing. IEEE Transactions on Cybernetics

  20. Chen H, Huang Z, Xu Y, Deng Z, Huang F, He P, Li Z (2022) Neighbor enhanced graph convolutional networks for node classification and recommendation. Knowledge-Based Systems 246:108594

    Article  Google Scholar 

  21. Zhang J, Xu Q (2021) Attention-aware heterogeneous graph neural network. Big Data Mining and Analytics 4(4):233–241

    Article  Google Scholar 

  22. Bielak P, Kajdanowicz T, Chawla NV (2022) Graph barlow twins: A self-supervised representation learning framework for graphs. Knowledge-Based Systems 256:109631

    Article  Google Scholar 

  23. Ji G, Liu K, He S, Zhao J (2016) Knowledge graph completion with adaptive sparse transfer matrix. In: Thirtieth AAAI Conference on Artificial Intelligence

  24. Xiao H, Huang M, Zhu X (2016) Transg: A generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2316–2325

  25. Wang P, Li S, Pan R (2018) Incorporating gan for negative sampling in knowledge representation learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32

  26. Cai L, Wang WY (2018) Kbgan: Adversarial learning for knowledge graph embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1470–1480

  27. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. Advances in neural information processing systems 30

  28. Qin S, Rao G, Bin C, Chang L, Gu T, Xuan W (2019) Knowledge graph embedding based on adaptive negative sampling. In: International Conference of Pioneering Computer Scientists, Engineers and Educators, pp 551–563

  29. Zhang Y, Yao Q, Shao Y, Chen L (2019) Nscaching: Simple and efficient negative sampling for knowledge graph embedding. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp 614–625

  30. Li C, Chen H, Li T, Yang X (2022) A stable community detection approach for complex network based on density peak clustering and label propagation. Appl Intell 52(2):1188–1208

    Article  Google Scholar 

  31. Ding J, He X, Yuan J, Chen Y, Jiang B (2018) Community detection by propagating the label of center. Physica A: Statistical Mechanics and its Applications 503:675–686

    Article  Google Scholar 

  32. Lü L, Zhang YC, Yeung CH, Zhou T (2011) Leaders in social networks, the delicious case. PloS one 6(6):21202

    Article  Google Scholar 

  33. Church KW (2017) Word2vec. Natural Language Engineering 23(1):155–162

  34. Zhao F, Jin L, Yang LT, Jin H (2022) Relation and entropy weight-aware knowledge graph embedding for cloud manufacturing. IEEE Transactions on Industrial Informatics 18(12):9047–9056

  35. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proceedings of the national academy of sciences 105(4):1118–1123

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62176236 and 62106225.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, 310023, Hangzhou, China

    Xu-Hua Yang, Gang-Feng Ma, Xin Jin, Hai-Xia Long, Jie Xiao & Lei Ye

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  1. Xu-Hua Yang
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  2. Gang-Feng Ma
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  3. Xin Jin
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  4. Hai-Xia Long
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  5. Jie Xiao
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Contributions

Xu-Hua Yang - Writing - Original Draft, Conceptualization, Funding acquisition. Gang-Feng Ma - Writing - Review & Editing, Validation, Formal analysis. Xin Jin - Methodology, Investigation. Hai-Xia Long - Supervision, Project administration, Funding acquisition. Jie Xiao - Resources, Data Curation. Lei Ye - Visualization.

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Correspondence to Xu-Hua Yang.

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Yang, XH., Ma, GF., Jin, X. et al. Knowledge graph embedding and completion based on entity community and local importance. Appl Intell 53, 22132–22142 (2023). https://doi.org/10.1007/s10489-023-04698-y

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