预算实体匹配中的域感知分布对齐理解

Computer Science > Databases

arXiv:2606.27342v1 (cs) [Submitted on 25 Jun 2026 (this version), latest version 30 Jun 2026 (v2)]

Title:Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching

Authors:Nicholas Pulsone, Gregory Goren, Roee Shraga View a PDF of the paper titled Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching, by Nicholas Pulsone and 2 other authors View PDF HTML (experimental)

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.

Subjects:	Databases (cs.DB); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:	arXiv:2606.27342 [cs.DB]
	(or arXiv:2606.27342v1 [cs.DB] for this version)
	https://doi.org/10.48550/arXiv.2606.27342 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Nicholas Pulsone Jr. [view email]
[v1] Thu, 25 Jun 2026 17:49:48 UTC (156 KB)
[v2] Tue, 30 Jun 2026 14:07:52 UTC (156 KB)
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