TY  - UNPD
A1  - Karapanagiotis, Pantelis
A1  - Liebald, Marius
T1  - Entity matching with similarity encoding: a supervised learning recommendation framework for linking (big) data
N2  - 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.
T3  - SAFE working paper - 398 
KW  - Entity matching
KW  - Entity resolution
KW  - Database linking
KW  - Machine learning
KW  - Record resolution
KW  - Similarity encoding
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/70399
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-703996
UR  - https://ssrn.com/abstract=4541376
N1  - We gratefully acknowledge research support from the Leibniz Institute for Financial Research SAFE.
PB  - SAFE
CY  - Frankfurt am Main
ER  -