research-article

Multi-Context Attention for Entity Matching

Authors:

Dongxiang Zhang,

Kian-Lee TanAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2634 - 2640

https://doi.org/10.1145/3366423.3380017

Published: 20 April 2020 Publication History

Abstract

Entity matching (EM) is a classic research problem that identifies data instances referring to the same real-world entity. Recent technical trend in this area is to take advantage of deep learning (DL) to automatically extract discriminative features. DeepER and DeepMatcher have emerged as two pioneering DL models for EM. However, these two state-of-the-art solutions simply incorporate vanilla RNNs and straightforward attention mechanisms. In this paper, we fully exploit the semantic context of embedding vectors for the pair of entity text descriptions. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea is further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments with 7 benchmark datasets that are publicly accessible. The experimental results clearly establish our superiority over DeepER and DeepMatcher in all the datasets.

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Cited By

Shahbazi NErfanian MAsudeh ANargesian FSrivastava D(2024)FairEM360: A Suite for Responsible Entity MatchingProceedings of the VLDB Endowment10.14778/3685800.368588917:12(4417-4420)Online publication date: 8-Nov-2024
https://doi.org/10.14778/3685800.3685889
Dou WShen DZhou XBai HKou YNie TCui HYu GSerra ESpezzano F(2024)Enhancing Deep Entity Resolution with Integrated Blocker-Matcher Training: Balancing Consensus and DiscrepancyProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679843(508-518)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679843
Fan GShraga RMiller R(2024)Gen-T: Table Reclamation in Data Lakes2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00272(3532-3545)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00272
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Index Terms

Multi-Context Attention for Entity Matching
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
2. Information systems
  1. Information retrieval
  2. Information systems applications

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

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Shahbazi NErfanian MAsudeh ANargesian FSrivastava D(2024)FairEM360: A Suite for Responsible Entity MatchingProceedings of the VLDB Endowment10.14778/3685800.368588917:12(4417-4420)Online publication date: 8-Nov-2024
https://doi.org/10.14778/3685800.3685889
Dou WShen DZhou XBai HKou YNie TCui HYu GSerra ESpezzano F(2024)Enhancing Deep Entity Resolution with Integrated Blocker-Matcher Training: Balancing Consensus and DiscrepancyProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679843(508-518)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679843
Fan GShraga RMiller R(2024)Gen-T: Table Reclamation in Data Lakes2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00272(3532-3545)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00272
Papadakis GKirielle NChristen PPalpanas T(2024)A Critical Re-evaluation of Record Linkage Benchmarks for Learning-Based Matching Algorithms2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00265(3435-3448)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00265
Sun LDai WMuhammad G(2024)Multi-level Graph Memory Network Cluster Convolutional Recurrent Network for traffic forecastingInformation Fusion10.1016/j.inffus.2023.102214105:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.102214
Zeakis APapadakis GSkoutas DKoubarakis M(2024)An in-depth analysis of pre-trained embeddings for entity resolutionThe VLDB Journal10.1007/s00778-024-00879-434:1Online publication date: 4-Dec-2024
https://doi.org/10.1007/s00778-024-00879-4
Wu SWu QDong HHua WZhou X(2023)Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity ResolutionProceedings of the VLDB Endowment10.14778/3632093.363209617:3(292-304)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.14778/3632093.3632096
Shahbazi NDanevski NNargesian FAsudeh ASrivastava D(2023)Through the Fairness Lens: Experimental Analysis and Evaluation of Entity MatchingProceedings of the VLDB Endowment10.14778/3611479.361152516:11(3279-3292)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611525
Zeakis APapadakis GSkoutas DKoubarakis M(2023)Pre-Trained Embeddings for Entity Resolution: An Experimental AnalysisProceedings of the VLDB Endowment10.14778/3598581.359859416:9(2225-2238)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.14778/3598581.3598594
Lee WTkachenko MLauw H(2023)Robust Bidirectional Poly-MatchingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326648035:10(10762-10774)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3266480
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