Entity Matching with Similarity Encoding: A Supervised Learning Recommendation Framework for Linking (Big) Data

Mon, 01 Jan 0001 00:00:00 +0000

Karapanagiotis, P., & Liebald, M. (2023). Entity Matching with Similarity Encoding: A Supervised Learning Recommendation Framework for Linking (Big) Data. SAFE Working Paper Series. No. 398. https://doi.org/10.2139/ssrn.4541376.

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.

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Entity Matching with Similarity Encoding: A Supervised Learning Recommendation Framework for Linking (Big) Data