Summary
This paper, authored by Nicholas Pulsone, Gregory Goren, and Roee Shraga, investigates how domain-aware distribution alignment behaves in budgeted entity matching (EM). Entity matching is a core operation in data integration pipelines, comparing records from different sources to determine whether they refer to the same real-world entity. Recent approaches have incorporated domain information and low-resource learning techniques to adapt EM systems to realistic settings, achieving strong performance. However, their behavior under varying data constraints and levels of supervision has remained unclear. The authors study this question and find that domain information can significantly improve matching performance across different data budgets and supervision levels. The work is available on arXiv as 2606.27342 and was published on 2026-06-27.
Paper Overview
Research Area: Machine Learning
Authors: Nicholas Pulsone, Gregory Goren, Roee Shraga
Published: 2026-06-27
arXiv:
2606.27342Abstract
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.
This paper studies the behavior of domain-aware distribution alignment in budgeted entity matching. The authors find that domain information can significantly improve matching performance under different data constraints and levels of supervision.
Links
- arXiv page: https://arxiv.org/abs/2606.27342
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