Skip to main content

Advertisement

Entity alignment in noisy knowledge graph

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Entity alignment is an important task in Knowledge Graph(KG), which aims to find identical entities in two different KGs. Existing methods include two steps, graph representation and alignment inference. The representation is learned based on the semantics and structure of KG. In applications, however, incorrect triples (which are also called structure noise) inevitably exist in KGs due to low-quality corpora and low-performance construction algorithms. The structure noise in KGs affects the representation of KGs and the alignment inference. To this end, we propose an entity alignment method in noisy knowledge graphs for the first time. Firstly, a noise-aware module is designed to recognize the noisy triples and exclude them from KG representation. Secondly, we design a more strict semi-supervised algorithm that combines local similarity and global alignment cost together to obtain high-quality pseudo-alignments in noisy environments. The experimental results demonstrate the effectiveness of our method in noisy KGs and the good compatibility with other baselines.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

This paper uses two publicly available datasets, DBP15k and SRPRS. All source code, datasets and models in this article can be downloaded from https://github.com/Zxl001103/RREA-NoisyTriples.

References

  1. Huynh VP, Papotti P (2019) Buckle: Evaluating fact checking algorithms built on knowledge bases. Proc VLDB Endow 12(12):1798–1801

  2. Zhang F, Yuan NJ, Lian D et al (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp 353–362

  3. Wang H, Zhang F, Xie X et al (2018) Dkn: Deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference. pp 1835–1844

  4. Han J, Cheng B, Wang X (2020) Open domain question answering based on text enhanced knowledge graph with hyperedge infusion. Findings of the Association for Computational Linguistics: EMNLP 2020. pp 1475–1481

  5. Zhang Y, Dai H, Kozareva Z et al (2018) Variational reasoning for question answering with knowledge graph. In: Proceedings of the AAAI conference on artificial intelligence

  6. Wang C, Song Y, Li H et al (2016) Text classification with heterogeneous information network kernels. In: Proceedings of the AAAI Conference on Artificial Intelligence

  7. Bollacker K, Cook R, Tufts P (2007) Freebase: A shared database of structured general human knowledge. In: AAAI. pp 1962–1963

  8. Lehmann J, Isele R, Jakob M et al (2015) Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web 6(2):167–195

  9. Heindorf S, Potthast M, Stein B et al (2016) Vandalism detection in wikidata. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. pp 327–336

  10. Mahdisoltani F, Biega J, Suchanek FM (2013) Yago3: A knowledge base from multilingual wikipedias. In: CIDR

  11. Carlson A, Betteridge J, Kisiel B et al (2010) Toward an architecture for never-ending language learning. In: Proceedings of the AAAI conference on artificial intelligence. pp 1306–1313

  12. Wu Y, Liu X, Feng Y et al (2019) Relation-aware entity alignment for heterogeneous knowledge graphs. arXiv:1908.08210

  13. Zhu R, Luo X, Ma M et al (2022) Adaptive graph convolutional network for knowledge graph entity alignment. Findings of the Association for Computational Linguistics: EMNLP 2022. pp 6011–6021

  14. Mao X, Wang W, Xu H et al (2020) Relational reflection entity alignment. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. pp 1095–1104

  15. Mao X, Wang W, Wu Y et al (2021) Boosting the speed of entity alignment 10\(\times \): Dual attention matching network with normalized hard sample mining. Proceedings of the Web Conference 2021. pp 821–832

  16. Sun Z, Hu W, Zhang Q et al (2018) Bootstrapping entity alignment with knowledge graph embedding. In: IJCAI

  17. Chen M, Tian Y, Yang M et al (2016) Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. arXiv:1611.03954

  18. Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci 577:852–870

  19. Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233–247

  20. Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl 80(20):31401–31433

  21. Ali A, Zhu Y, Chen Q et al (2019) Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks. In: 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS). IEEE, pp 125–132

  22. Alsarhan T, Harfoushi O, Shdefat AY et al (2023) Improved graph convolutional network with enriched graph topology representation for skeleton-based action recognition. Electronics 12(4):879

  23. Wang Z, Lv Q, Lan X et al (2018) Cross-lingual knowledge graph alignment via graph convolutional networks. In: Proceedings of the 2018 conference on empirical methods in natural language processing. pp 349–357

  24. Mao X, Wang W, Xu H et al (2020) Mraea: an efficient and robust entity alignment approach for cross-lingual knowledge graph. In: Proceedings of the 13th International Conference on Web Search and Data Mining. pp 420–428

  25. He K, Zhang X, Ren S et al (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. pp 1026–1034

  26. Tang W, Su F, Sun H et al (2023) Weakly supervised entity alignment with positional inspiration. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. pp 814–822

  27. Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). pp 1532–1543

  28. Feng F, Yang Y, Cer D et al (2020) Language-agnostic bert sentence embedding. arXiv:2007.01852

  29. Zhou Y, Zhu C, Zhu W et al (2024) Scmea: A stacked co-enhanced model for entity alignment based on multi-aspect information fusion and bidirectional contrastive learning. Neural Netw 173:106178

  30. Xin K, Sun Z, Hua W et al (2022) Large-scale entity alignment via knowledge graph merging, partitioning and embedding. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. pp 2240–2249

  31. Qi D, Chen S, Sun X et al (2023) A multiscale convolutional gragh network using only structural information for entity alignment. Appl Intell 53(7):7455–7465

  32. Zakarya M, Khan AA, Qazani MRC et al (2024) Sustainable computing across datacenters: A review of enabling models and techniques. Comput Sci Rev 52:100620

  33. Ge C, Liu X, Chen L et al (2021) Largeea: Aligning entities for large-scale knowledge graphs. arXiv:2108.05211

  34. Zhu H, Xie R, Liu Z et al (2017) Iterative entity alignment via joint knowledge embeddings. In: IJCAI. pp 4258–4264

  35. Mao X, Wang W, Wu Y et al (2022) Lightea: A scalable, robust, and interpretable entity alignment framework via three-view label propagation. arXiv:2210.10436

  36. Ahmed N, Rozina Ali A et al (2023) Images denoising for covid-19 chest x-ray based on multi-scale parallel convolutional neural network. Multimedia Syst 29(6):3877–3890

  37. Chen D, Chen Y, Xue D (2015) Fractional-order total variation image denoising based on proximity algorithm. Appl Math Comput 257:537–545

  38. Dong F, Chen Y (2016) A fractional-order derivative based variational framework for image denoising. Invers Prob Imaging 10(1)

  39. Li D, Tian X, Jin Q et al (2018) Adaptive fractional-order total variation image restoration with split bregman iteration. ISA Trans 82:210–222

  40. Lin J, Wan Y, Xu J et al (2023) Long-tailed graph neural networks via graph structure learning for node classification. Appl Intell 1–17

  41. Ge Y, Chen D, Li H (2020) Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. arXiv:2001.01526

  42. Han J, Li YL, Wang S (2022) Delving into probabilistic uncertainty for unsupervised domain adaptive person re-identification. In: Proceedings of the AAAI conference on artificial intelligence. pp 790–798

  43. Ghosh A, Kumar H, Sastry PS (2017) Robust loss functions under label noise for deep neural networks. In: Proceedings of the AAAI conference on artificial intelligence

  44. Zhang Z, Sabuncu M (2018) Generalized cross entropy loss for training deep neural networks with noisy labels. Adv Neural Inf Process Syst 31

  45. Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2818–2826

  46. Bordes A, Usunier N, Garcia-Duran A et al (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inf Process Syst 26

  47. Guo A, Tan Z, Zhao X (2020) Measuring triplet trustworthiness in knowledge graphs via expanded relation detection. In: Knowledge Science, Engineering and Management: 13th International Conference, KSEM 2020, Hangzhou, China, August 28–30, 2020, Proceedings, Part I 13. Springer, pp 65–76

  48. Zhao Y, Liu J (2019) Scef: A support-confidence-aware embedding framework for knowledge graph refinement. arXiv:1902.06377

  49. Wang Y, Ma F, Gao J (2020) Efficient knowledge graph validation via cross-graph representation learning. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. pp 1595–1604

  50. Zhang Q, Dong J, Duan K et al (2022) Contrastive knowledge graph error detection. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. pp 2590–2599

  51. Pei S, Yu L, Yu G et al (2020) Rea: Robust cross-lingual entity alignment between knowledge graphs. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp 2175–2184

  52. Pei S, Yu L, Yu G et al (2022) Graph alignment with noisy supervision. In: Proceedings of the ACM Web Conference 2022. pp 1104–1114

  53. Liu X, Zhang K, Liu Y et al (2023) Rhgn: Relation-gated heterogeneous graph network for entity alignment in knowledge graphs. In: Findings of the Association for Computational Linguistics: ACL 2023. pp 8683–8696

  54. Zhang R, Trisedya BD, Li M et al (2022) A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning. VLDB J 31(5):1143–1168

  55. Zhang Y, Wu J, Yu K et al (2022) Independent relation representation with line graph for cross-lingual entity alignment. IEEE Trans Knowl Data Eng

  56. Van Tong V, Huynh TT, Nguyen TT et al (2021) Incomplete knowledge graph alignment. arXiv:2112.09266

  57. Conneau A, Lample G, Ranzato M et al (2017) Word translation without parallel data. arXiv:1710.04087

  58. Zhang Y, Wu J, Yu K et al (2023) Diverse structure-aware relation representation in cross-lingual entity alignment. ACM Trans Knowl Disc Data

  59. Sun Z, Hu W, Li C (2017) Cross-lingual entity alignment via joint attribute-preserving embedding. In: The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I 16. Springer, pp 628–644

  60. Guo L, Sun Z, Hu W (2019) Learning to exploit long-term relational dependencies in knowledge graphs. In: International conference on machine learning. PMLR, pp 2505–2514

  61. Li C, Cao Y, Hou L et al (2019) Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model. Assoc Comput Linguist

  62. Cao Y, Liu Z, Li C et al (2019) Multi-channel graph neural network for entity alignment. arXiv:1908.09898

  63. Sun Z, Wang C, Hu W, et al (2020) Knowledge graph alignment network with gated multi-hop neighborhood aggregation. In: Proceedings of the AAAI conference on artificial intelligence. pp 222–229

  64. Zhu R, Ma M, Wang P (2021) Raga: relation-aware graph attention networks for global entity alignment. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp 501–513

  65. Huang Y, Zhang X, Zhang R et al (2024) Progressively modality freezing for multi-modal entity alignment. arXiv:2407.16168

  66. Zhang S, Tong H (2016) Final: Fast attributed network alignment. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp 1345–1354

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (under grant 61976077,62076085,62076087), the Natural Science Foundation of Anhui Province (under grant 2208085MF170) and the University Synergy Innovation Program of Anhui Province (GXXT-2022-040).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiaolong Zhu, Yuhong Zhang and Xuegang Hu. The first draft of the manuscript was written by Xiaolong Zhu and the manuscript was revised by Yuhong Zhang and Xuegang Hu. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yuhong Zhang.

Ethics declarations

Competing Interests

The authors declare that there is no conflict of interests or personal relationships regarding the publication of the paper.

Compliance with Ethical Standards

This article does not contain any studies with human participants or animals performed by any of the authors and the datasets are publicly available and widely used.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Zhu, X. & Hu, X. Entity alignment in noisy knowledge graph. Appl Intell 55, 210 (2025). https://doi.org/10.1007/s10489-024-06131-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-06131-4

Keywords