---
title: "预算实体匹配中的域感知分布对齐理解"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.27342v1"
permalink: "https://feed.astralor.com/items/itm_688be3813b5d51a065f88254"
published: "2026-06-25T17:49:48.000Z"
score: 50
channel: "tech"
tags: ["BEACON","分布对齐","域感知","实体匹配","预算约束"]
---

# 预算实体匹配中的域感知分布对齐理解

## 速览

本文研究了低资源、域感知的实体匹配（EM）方法——BEACON，并系统分析了算法选择和数据可用性条件对其性能的影响。通过一系列针对性实验，深入揭示了分布对齐在预算约束下的作用机制和BEACON框架的行为特征，为实际部署提供了理论指导。

## 关键点

- BEACON是面向低资源场景的域感知实体匹配方法。
- 系统评估了不同算法配置和数据约束下的性能变化。
- 发现分布对齐对预算EM的鲁棒性有显著影响。

## 全文

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.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_688be3813b5d51a065f88254