Entity Resolution in Large Patent Databases: An Optimization Approach

Emiel Caron; Ekaterini Ioannou

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Paper

Entity Resolution in Large Patent Databases: An Optimization Approach

Topics: Big Data; Coupling and Integrating Heterogeneous Data Sources; Data Mining; Large Scale Databases

In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, 148-156, 2021

Authors: Emiel Caron and Ekaterini Ioannou

Affiliation: Department of Management, Tilburg University, Tilburg, The Netherlands

Keyword(s): Entity Resolution, Data Disambiguation, Data Cleaning, Data Integration, Bibliographic Databases.

Abstract: Entity resolution in databases focuses on detecting and merging entities that refer to the same real-world object. Collective resolution is among the most prominent mechanisms suggested to address this challenge since the resolution decisions are not made independently, but are based on the available relationships within the data. 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.

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Paper citation in several formats:

Caron, E. and Ioannou, E. (2021). Entity Resolution in Large Patent Databases: An Optimization Approach. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 148-156. DOI: 10.5220/0010527501480156

@conference{iceis21,
author={Emiel Caron. and Ekaterini Ioannou.},
title={Entity Resolution in Large Patent Databases: An Optimization Approach},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={148-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010527501480156},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Entity Resolution in Large Patent Databases: An Optimization Approach
SN - 978-989-758-509-8
IS - 2184-4992
AU - Caron, E.
AU - Ioannou, E.
PY - 2021
SP - 148
EP - 156
DO - 10.5220/0010527501480156
PB - SciTePress