Entity Resolution in Large Patent Databases: an optimization approach (Accepted for ICEIS 2021, April 26-28):

Candidate to ICEIS 2021 best paper award

Authors: Emiel Caron and Ekaterini Ioannou

Abstract: Entity resolution focuses on detecting and merging entities that refer to the same real-world object. Collective resolution is among the most prominent mechanisms suggested for address this challenge since the resolution decisions are not made independently but are based on available relationships. In this paper we introduce a
novel resolution approach that combines the essence of collective resolution with rules and transformations among entity attributes and values. We illustrate how the approach’s parameters are optimized based on a global optimization algorithm, i.e., simulated annealing, and explain how this optimization is performed using a small training set. The quality of the approach is verified through an extensive experimental evaluation with 40M real-world scientific entities from the Patstat database.

Keywords: Entity Resolution, Data Disambiguation, Data Cleaning, Data Integration, Bibliographic Databases.

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