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Neural Networks for Entity Matching: A Survey

Barlaug, Nils; Gulla, Jon Atle
Peer reviewed, Journal article
Accepted version
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URI
https://hdl.handle.net/11250/2755473
Date
2021
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  • Institutt for datateknologi og informatikk [6323]
  • Publikasjoner fra CRIStin - NTNU [34929]
Original version
https://doi.org/10.1145/3442200
Abstract
Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge.

In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.
Publisher
Association for Computing Machinery (ACM)
Journal
ACM Transactions on Knowledge Discovery from Data

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