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Robust Bidirectional Poly-Matching
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AI
DOI：10.1109/TKDE.2023.3266480.png)
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Abstract
En 中文
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.
Keywords:
Lenses
Cameras
Noise measurement
Matched filters
Soft sensors
Mathematical models
Linear programming
Entity resolution
matching
one-to-many
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