Robust Bidirectional Poly-Matching

• Main Authors: LEE, Ween Jiann, TKACHENKO, Maksim, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Lenses; Cameras; Noise measurement; Matched filters; Soft sensors; Mathematical models; Linear programming; Entity resolution; matching; one-to-many; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8277; https://ink.library.smu.edu.sg/context/sis_research/article/9280/viewcontent/Robust_bidirectional_poly_av.pdf
• Institution: Singapore Management University

A fundamental problem in many scenarios is to match entities across two data sources. It is frequently presumed in prior work that entities to be matched are of comparable granularity. In this work, we address one-to-many or poly-matching in the scenario where entities have varying granularity. A distinctive feature of our problem is its bidirectional nature, where the 'one' or the 'many' could come from either source arbitrarily. Moreover, to deal with diverse entity representations that give rise to noisy similarity values, we incorporate novel notions of receptivity and reclusivity into a robust matching objective. As the optimal solution to the resulting formulation is proven computationally intractable, we propose more scalable yet still performant heuristics. Experiments on multiple real-life datasets showcase the effectiveness and outperformance of our proposed algorithms over baselines.