Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching
2026-06-25 • Databases
DatabasesArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors studied a method called BEACON, which helps computers decide if records from different places talk about the same real-world thing, even when there's not much data or background info available. They tested how well BEACON works when changing certain settings and how much data it has to learn from. Their experiments helped explain how parts of the method, like matching data distributions, impact its success. This gives a clearer picture of how BEACON behaves in different practical situations.
Entity MatchingData IntegrationLow-Resource LearningDomain AwarenessDistribution AlignmentBEACON FrameworkSupervised LearningAlgorithmic Choices
Authors
Nicholas Pulsone, Gregory Goren, Roee Shraga
Abstract
Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they refer to the same real-world entity. Recent work has incorporated domain information and low-resource learning techniques to better adapt EM systems to realistic settings. While these approaches have demonstrated strong performance, it remains unclear how they behave under varying data constraints and levels of supervision in practice. In this paper, we investigate a state-of-the-art method for low-resource, domain-aware EM--BEACON--and study how its performance is affected by different algorithmic choices and data availability conditions. We conduct a series of targeted experiments to evaluate these variations, providing deeper insight into the role of distribution alignment and the behavior of the BEACON framework.