Effective entity matching with transformers

Li, Yuliang; Li, Jinfeng; Suhara, Yoshi; Doan, AnHai; Tan, Wang-Chiew

doi:10.1007/s00778-023-00779-z

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Literature Review1

Deep Learning-based Entity Matching1

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2023

2024

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Cited by 11 publications

(2 citation statements)

References 52 publications

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“…Entity Resolution with Path-based Similarities: Li et al [19] introduce path-based similarity measures that consider connections between entities on a contextual graph. This approach improves recall compared to traditional methods, but a comparison with recent deep learning techniques is missing.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Machine Learning Approaches for Semantics-Based String Matching

Srivatsana,

K U

2024

Int. J. Res. Publ. Rev.

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Section: Literature Reviewmentioning

confidence: 99%

A Review of Machine Learning Approaches for Semantics-Based String Matching

Srivatsana,

K U

2024

Int. J. Res. Publ. Rev.

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“…This method fine-tunes pre-trained LM Sentence-BERT [37] for entity matching tasks [38], which allows domain knowledge to be added by highlighting important pieces of the input that may be useful for matching decisions. Li et al [39] propose a pre-trained Transformer language model for effective entity matching and obtaining higher F1 score on two large datasets. Refs.…”

Section: Deep Learning-based Entity Matchingmentioning

confidence: 99%

Entity Matching by Pool-Based Active Learning

Han,

2024

Electronics

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The goal of entity matching is to find the corresponding records representing the same entity from different data sources. At present, in the mainstream methods, rule-based entity matching methods need tremendous domain knowledge. Machine-learning-based or deep-learning-based entity matching methods need a large number of labeled samples to build the model, which is difficult to achieve in some applications. In addition, learning-based methods are more likely to overfit, so the quality requirements of training samples are very high. In this paper, we present an active learning method for entity matching tasks. This method needs to manually label only a small number of valuable samples, and use these labeled samples to build a model with high quality. This paper proposes hybrid uncertainty as a query strategy to find those valuable samples for labeling, which can minimize the number of labeled training samples and at the same time meet the requirements of entity matching tasks. The proposed method is validated on seven data sets in different fields. The experiments show that the proposed method uses only a small number of labeled samples and achieves better effects compared to current existing approaches.

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Evaluation of Candidate Pair Generation Strategies in Entity Matching

Chantaranimi,

Natwichai

2023

Lecture Notes on Data Engineering and Communications Technologies

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Effective entity matching with transformers

Cited by 11 publications

References 52 publications

A Review of Machine Learning Approaches for Semantics-Based String Matching

A Review of Machine Learning Approaches for Semantics-Based String Matching

Entity Matching by Pool-Based Active Learning

Evaluation of Candidate Pair Generation Strategies in Entity Matching

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