research-article

Uncertainty-aware Pseudo Label Refinery for Entity Alignment

Authors:

Dandan SongAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 829 - 837

https://doi.org/10.1145/3485447.3511926

Published: 25 April 2022 Publication History

Abstract

Entity alignment (EA), which aims to discover equivalent entities in knowledge graphs (KGs), bridges heterogeneous sources of information and facilitates the integration of knowledge. Recently, based on translational models, EA has achieved impressive performance in utilizing graph structures or by adopting auxiliary information. However, existing entity alignment methods mainly rely on manually labeled entity alignment seeds, limiting their applicability in real scenarios. In this paper, a simple but effective Uncertainty-aware Pseudo Label Refinery (UPLR) framework is proposed without manually labeling requirement and is capable of learning high-quality entity embeddings from pseudo-labeled data sets containing noisy data. Our proposed model relies on two key factors: First, a non-sampling calibration strategy is provided that does not require artificially designed thresholds to reduce the influence of noise labels. Second, the entity alignment model achieves goal-oriented uncertainty correction through a gradual enhancement strategy. Experimental results on benchmark datasets demonstrate that our proposed model outperforms the existing supervised methods in cross-lingual knowledge graph tasks. Our source code is available at: https://github.com/Jia-Li2/UPLR/.

References

[1]

Philip Bachman, Ouais Alsharif, and Doina Precup. 2014. Learning with pseudo-ensembles. Advances in neural information processing systems 27 (2014), 3365–3373.

[2]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NIPS, 2013.

Digital Library

[3]

Yixin Cao, Zhiyuan Liu, Chengjiang Li, Juanzi Li, and Tat-Seng Chua. 2019. Multi-channel graph neural network for entity alignment. In ACL, 2019.

[4]

Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018. Deep clustering for unsupervised learning of visual features. In ECCV, 2018.

Digital Library

[5]

Muhao Chen, Yingtao Tian, Mohan Yang, and Carlo Zaniolo. 2017. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. In IJCAI, 2017.

Digital Library

[6]

Soham Datta, Prabir Mallick, Sangameshwar Patil, Indrajit Bhattacharya, and Girish Palshikar. 2021. Generating An Optimal Interview Question Plan Using A Knowledge Graph And Integer Linear Programming. In NAACL, 2021.

[7]

Hao Fei, Yafeng Ren, Yue Zhang, Donghong Ji, and Xiaohui Liang. 2021. Enriching contextualized language model from knowledge graph for biomedical information extraction. Briefings in Bioinformatics 22, 3 (2021).

[8]

Matthias Fey, Jan E Lenssen, Christopher Morris, Jonathan Masci, and Nils M Kriege. 2020. Deep graph matching consensus. In ECCV, 2020.

[9]

Fan Gong, Meng Wang, Haofen Wang, Sen Wang, and Mengyue Liu. 2021. SMR: Medical knowledge graph embedding for safe medicine recommendation. Big Data Research 23(2021), 100174.

[10]

Lingbing Guo, Zequn Sun, and Wei Hu. 2019. Learning to exploit long-term relational dependencies in knowledge graphs. In ICML, 2019.

[11]

Hangfeng He and Xu Sun. 2017. A unified model for cross-domain and semi-supervised named entity recognition in chinese social media. In AAAI, 2017.

[12]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In ICDM, 2008.

Digital Library

[13]

Samuli Laine and Timo Aila. 2016. Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242(2016).

[14]

Dong-Hyun Lee 2013. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In ICML, 2013.

[15]

Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, and Tat-Seng Chua. 2019. Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model. In EMNLP, 2019.

[16]

Xixun Lin, Hong Yang, Jia Wu, Chuan Zhou, and Bin Wang. 2019. Guiding cross-lingual entity alignment via adversarial knowledge embedding. In ICDM, 2019.

[17]

Fangyu Liu, Muhao Chen, Dan Roth, and Nigel Collier. 2021. Visual Pivoting for (Unsupervised) Entity Alignment. In AAAI, 2021.

[18]

Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, and Tat-Seng Chua. 2020. Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment. In EMNLP, 2020.

[19]

Xin Mao, Wenting Wang, Yuanbin Wu, and Man Lan. 2021. Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining. In WWW, 2021.

Digital Library

[20]

Xin Mao, Wenting Wang, Yuanbin Wu, and Man Lan. 2021. From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment. In EMNLP, 2021.

[21]

Xin Mao, Wenting Wang, Huimin Xu, Man Lan, and Yuanbin Wu. 2020. MRAEA: an efficient and robust entity alignment approach for cross-lingual knowledge graph. In WSDM, 2020.

Digital Library

[22]

Xin Mao, Wenting Wang, Huimin Xu, Yuanbin Wu, and Man Lan. 2020. Relational Reflection Entity Alignment. In CIKM, 2020.

Digital Library

[23]

Shichao Pei, Lu Yu, Robert Hoehndorf, and Xiangliang Zhang. 2019. Semi-supervised entity alignment via knowledge graph embedding with awareness of degree difference. In WWW, 2019.

[24]

Shichao Pei, Lu Yu, Guoxian Yu, and Xiangliang Zhang. 2020. Rea: Robust cross-lingual entity alignment between knowledge graphs. In SIGKDD, 2020.

Digital Library

[25]

Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yuejia Xiang, Ningyu Zhang, and Yefeng Zheng. 2021. Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding. In IJCAI, 2021.

[26]

Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, and Nanning Zheng. 2018. Transductive semi-supervised deep learning using min-max features. In ECCV, 2018.

Digital Library

[27]

Ke Sun, Zhouchen Lin, and Zhanxing Zhu. 2020. Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In AAAI, 2020.

[28]

Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, and Wei Zhang. 2020. Knowledge Association with Hyperbolic Knowledge Graph Embeddings. In EMNLP, 2020.

[29]

Z. Sun, W. Hu, and C. Li. 2017. Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding. Springer, Cham (2017).

[30]

Zequn Sun, Wei Hu, and Chengkai Li. 2017. Cross-lingual entity alignment via joint attribute-preserving embedding. In ISWC, 2017.

Digital Library

[31]

Zequn Sun, Wei Hu, Qingheng Zhang, and Yuzhong Qu. 2018. Bootstrapping Entity Alignment with Knowledge Graph Embedding. In IJCAI, 2018.

Digital Library

[32]

Zequn Sun, Jiacheng Huang, Wei Hu, Muhao Chen, Lingbing Guo, and Yuzhong Qu. 2019. Transedge: Translating relation-contextualized embeddings for knowledge graphs. In ISWC, 2019.

Digital Library

[33]

Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, and Yuzhong Qu. 2020. Knowledge graph alignment network with gated multi-hop neighborhood aggregation. In AAAI, 2020.

[34]

Antti Tarvainen and Harri Valpola. 2017. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. arXiv preprint arXiv:1703.01780(2017).

[35]

B. D. Trsedya, J. Qi, and Z. Rui. 2019. Entity Alignment between Knowledge Graphs Using Attribute Embeddings. In AAAI, 2019.

[36]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903(2017).

[37]

Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang. 2018. Cross-lingual knowledge graph alignment via graph convolutional networks. In EMNLP, 2018.

[38]

Zhichun Wang, Jinjian Yang, and Xiaoju Ye. 2020. Knowledge Graph Alignment with Entity-Pair Embedding. In EMNLP, 2020.

[39]

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, and Dongyan Zhao. 2019. Relation-aware entity alignment for heterogeneous knowledge graphs. In IJCAI, 2019.

[40]

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, and Dongyan Zhao. 2019. Jointly learning entity and relation representations for entity alignment. In EMNLP, 2019.

[41]

Abhinav Gupta Xiaolong Wang, Ross Girshickand Kaiming He. 2018. Non-local Neural Networks. In CVPR, 2018.

[42]

Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le. 2020. Self-training with noisy student improves imagenet classification. In CVPR, 2020.

[43]

Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, and Dong Yu. 2019. Cross-lingual knowledge graph alignment via graph matching neural network. In ACL, 2019.

[44]

Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, and Dong Yu. 2019. Cross-lingual knowledge graph alignment via graph matching neural network. In ACL, 2019.

[45]

Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, and Hanghang Tong. 2021. Dynamic Knowledge Graph Alignment. In AAAI, 2021.

[46]

Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, and Xu Sun. 2020. Aligning cross-lingual entities with multi-aspect information. In EMNLP, 2020.

[47]

Wexin Zeng, Xiang Zhao, Jiuyang Tang, and Xuemin Lin. 2020. Collective Embedding-based Entity Alignment via Adaptive Features. In ICDE, 2020.

[48]

Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, and Li Guo. 2019. Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs. In IJCAI, 2019.

[49]

Yao Zhu, Hongzhi Liu, Zhonghai Wu, and Yingpeng Du. 2021. Relation-Aware Neighborhood Matching Model for Entity Alignment. In AAAI, 2021.

Cited By

Cai WZhou RMa W(2025)Efficient and Effective Unsupervised Entity Alignment in Large Knowledge GraphsApplied Sciences10.3390/app1504197615:4(1976)Online publication date: 13-Feb-2025
https://doi.org/10.3390/app15041976
Song JBai LAn XZhou L(2025)Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learningNeurocomputing10.1016/j.neucom.2024.129019617(129019)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129019
Li ZDing NLiang CCao SZhai MHuang RZhang ZHu B(2025)A semi-supervised framework fusing multiple information for knowledge graph entity alignmentExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125282259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125282
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Index Terms

Uncertainty-aware Pseudo Label Refinery for Entity Alignment
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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National Key Research and Development Program of China
National Natural Science Foundation of China
Beijing Academy of Artificial Intelligence (BAAI)

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WWW '22

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SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Cai WZhou RMa W(2025)Efficient and Effective Unsupervised Entity Alignment in Large Knowledge GraphsApplied Sciences10.3390/app1504197615:4(1976)Online publication date: 13-Feb-2025
https://doi.org/10.3390/app15041976
Song JBai LAn XZhou L(2025)Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learningNeurocomputing10.1016/j.neucom.2024.129019617(129019)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129019
Li ZDing NLiang CCao SZhai MHuang RZhang ZHu B(2025)A semi-supervised framework fusing multiple information for knowledge graph entity alignmentExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125282259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125282
Zhu BTian RYuan XHan RYang YFu B(2025)Cross-lingual entity alignment based on complex relationships and fine-grained attributesApplied Soft Computing10.1016/j.asoc.2025.112894172(112894)Online publication date: Mar-2025
https://doi.org/10.1016/j.asoc.2025.112894
Cai WXu J(2024)A Unified Framework for Entity Alignment of Knowledge GraphsProceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence10.1145/3709026.3709069(221-228)Online publication date: 6-Dec-2024
https://dl.acm.org/doi/10.1145/3709026.3709069
Zhang YWu JYu KWu X(2024)Diverse Structure-Aware Relation Representation in Cross-Lingual Entity AlignmentACM Transactions on Knowledge Discovery from Data10.1145/363877818:4(1-23)Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1145/3638778
Hu XSun Y(2024)Cross-language entity alignment based on graph convolution neural network and attribute informationInternational Conference on Computer Network Security and Software Engineering (CNSSE 2024)10.1117/12.3031901(12)Online publication date: 6-Jun-2024
https://doi.org/10.1117/12.3031901
Zhang RSu YTrisedya BZhao XYang MCheng HQi J(2024)AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment Enabled by Large Language ModelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332548436:6(2357-2371)Online publication date: Jun-2024
https://doi.org/10.1109/TKDE.2023.3325484
Liang YCai WYang MJiang Y(2024)An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignmentNeural Networks10.1016/j.neunet.2024.106583179(106583)Online publication date: Nov-2024
https://doi.org/10.1016/j.neunet.2024.106583
Zhou YZhu CZhu WLi H(2024)SCMEA: A stacked co-enhanced model for entity alignment based on multi-aspect information fusion and bidirectional contrastive learningNeural Networks10.1016/j.neunet.2024.106178173(106178)Online publication date: May-2024
https://doi.org/10.1016/j.neunet.2024.106178
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