Entity matching with similarity encoding: A supervised learning recommendation framework for linking (big) data

Pantelis Karapanagiotis and Marius Liebald

No 398, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

Abstract: In this study, we introduce a novel entity matching (EM) framework. It com-bines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are preva-lent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encod-ing is constructed using (edit) distances instead of semantic similarities, it avoids out-of-vocabulary problems when matching dirty data. We highlight this property by applying an EM application to dirty financial firm-level data extracted from historical archives.

Keywords: Entity matching; Entity resolution; Database linking; Machine learning; Record resolution; Similarity encoding (search for similar items in EconPapers)
JEL-codes: C8 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:398

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