research-article

Deep Entity Matching: Challenges and Opportunities

Authors:

Yoshihiko Suhara,

Wang-Chiew TanAuthors Info & Claims

Journal of Data and Information Quality (JDIQ), Volume 13, Issue 1

Article No.: 1, Pages 1 - 17

https://doi.org/10.1145/3431816

Published: 06 January 2021 Publication History

Abstract

Entity matching refers to the task of determining whether two different representations refer to the same real-world entity. It continues to be a prevalent problem for many organizations where data resides in different sources and duplicates the need to be identified and managed. The term “entity matching” also loosely refers to the broader problem of determining whether two heterogeneous representations of different entities should be associated together. This problem has an even wider scope of applications, from determining the subsidiaries of companies to matching jobs to job seekers, which has impactful consequences.

In this article, we first report our recent system DITTO, which is an example of a modern entity matching system based on pretrained language models. Then we summarize recent solutions in applying deep learning and pre-trained language models for solving the entity matching task. Finally, we discuss research directions beyond entity matching, including the promise of synergistically integrating blocking and entity matching steps together, the need to examine methods to alleviate steep training data requirements that are typical of deep learning or pre-trained language models, and the importance of generalizing entity matching solutions to handle the broader entity matching problem, which leads to an even more pressing need to explain matching outcomes.

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Wang YZhou LWang YPeng Z(2024)Leveraging Pretrained Language Models for Enhanced Entity MatchingInternational Journal of Intelligent Systems10.1155/2024/19412212024Online publication date: 1-Jan-2024
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Moslemi MMilani M(2024)Threshold-Independent Fair Matching through Score CalibrationProceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI10.1145/3665601.3669845(40-44)Online publication date: 9-Jun-2024
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Index Terms

Deep Entity Matching: Challenges and Opportunities
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Data management systems
    1. Information integration
      1. Deduplication
      2. Entity resolution

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cover image Journal of Data and Information Quality

Journal of Data and Information Quality Volume 13, Issue 1

On the Horizon, On the Horizon and Experience Papers

March 2021

104 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/3446835

Editor:
Tiziana Catarci
Sapienza University of Rome, Rome, Italy

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Publication History

Published: 06 January 2021

Accepted: 01 October 2020

Revised: 01 October 2020

Received: 01 October 2020

Published in JDIQ Volume 13, Issue 1

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Wang YZhou LWang YPeng Z(2024)Leveraging Pretrained Language Models for Enhanced Entity MatchingInternational Journal of Intelligent Systems10.1155/2024/19412212024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/1941221
Moslemi MMilani M(2024)Threshold-Independent Fair Matching through Score CalibrationProceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI10.1145/3665601.3669845(40-44)Online publication date: 9-Jun-2024
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Widodo PRakhmawati N(2024)Creating A Patient Data Redundancy Detection Model using Deep Learning Methods2024 7th International Conference on Informatics and Computational Sciences (ICICoS)10.1109/ICICoS62600.2024.10636911(262-266)Online publication date: 17-Jul-2024
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Zhang MJi ZLuo ZWu YChai C(2024)Applications and Challenges for Large Language Models: From Data Management Perspective2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00441(5530-5541)Online publication date: 13-May-2024
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Shahbazi NWang JMiao ZBhutani N(2024)Fairness-Aware Data Preparation for Entity Matching2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00268(3476-3489)Online publication date: 13-May-2024
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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
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