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2018 | OriginalPaper | Chapter

Deep Learning Based Approach for Entity Resolution in Databases

Authors : Nihel Kooli, Robin Allesiardo, Erwan Pigneul

Published in: Intelligent Information and Database Systems

Publisher: Springer International Publishing

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Abstract

This paper proposes a Deep Neural Networks (DNN) based approach for entity resolution in databases. This approach is mainly based on a record linkage process which aims to detect records that refer to the same entity. First, record pairs are represented by their word embedding using an N-gram embedding based method. Then, they are classified into matching or unmatching pairs using a DNN model. Three DNN architectures: Multi-Layer Perceptron, Long Short Term Memory networks and Convolutional Neural Networks are investigated and compared for this purpose. The approach is experimented on two databases. The results exceed \(97\%\) for recall and \(96\%\) for precision. The comparison with similarity measure and classical classifier based approaches shows a significant improvement in the results on the two databases.

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Footnotes
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Metadata
Title
Deep Learning Based Approach for Entity Resolution in Databases
Authors
Nihel Kooli
Robin Allesiardo
Erwan Pigneul
Copyright Year
2018
Book
Intelligent Information and Database Systems

Print ISBN: 978-3-319-75419-2

Electronic ISBN: 978-3-319-75420-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75420-8_1

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