Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching

2026-06-25

AI Summary

This study addresses the unclear role of distribution alignment in domain-aware entity matching under low-resource and varying supervision conditions. The authors systematically evaluate the BEACON framework across diverse data constraints and algorithmic configurations, offering the first in-depth analysis of the factors influencing distribution alignment in budget-constrained settings. Through controlled experiments, they uncover the synergistic mechanism between domain information and distribution alignment, demonstrating its critical impact on matching performance. The findings provide empirical evidence and practical design guidance for optimizing entity matching systems in low-resource, multi-domain scenarios.

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.

Problem

• Entity Matching
• Domain Awareness
• Low-Resource Learning
• Distribution Alignment
• Data Constraints

Innovation

• domain-aware
• distribution alignment
• budgeted entity matching
• low-resource learning
• BEACON