Arbeitspapier

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

to connected objects

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.

Language: Englisch

Bibliographic citation: Series: SAFE Working Paper ; No. 398

Classification: Wirtschaft

Subject: Entity matching
Entity resolution
Database linking
Machine learning
Record resolution
Similarity encoding

Event: Geistige Schöpfung

(who): Karapanagiotis, Pantelis
Liebald, Marius

Event: Veröffentlichung

(who): Leibniz Institute for Financial Research SAFE

(where): Frankfurt a. M.

(when): 2023

Handle: http://hdl.handle.net/10419/274537

Last update: 10.03.2025, 11:46 AM CET

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Object type

Arbeitspapier

Associated

Karapanagiotis, Pantelis
Liebald, Marius
Leibniz Institute for Financial Research SAFE

Time of origin

2023

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