Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching
For practitioners in data integration, this work provides insights into the behavior of domain-aware entity matching under realistic constraints, but it is an incremental analysis of an existing method.
The paper investigates BEACON, a state-of-the-art method for low-resource, domain-aware entity matching, and studies how its performance varies with different algorithmic choices and data availability conditions through targeted experiments.
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.