评估实体匹配中阻断技术的偏见，推动公平数据整合
Evaluating Blocking Biases in Entity Matching
- 扩展传统阻断指标，加入公平性维度
- 发现部分阻断方法对特定群体存在隐性偏好
- 适合关注数据公平性的研究人员和工程师
实体匹配（EM）对于识别不同数据源中的等效实体至关重要，随着数据量增长和异构性加剧，这一任务愈发困难。阻断技术通过降低计算复杂度，使EM可扩展化，是关键环节。尽管阻断方法不断进步，但其可能对某些人口群体产生不公平影响的问题长期被忽视。本研究将传统阻断评估指标拓展至包含公平性维度，提出一套评估阻断技术偏见的框架。通过实验分析，评估了多种阻断方法的有效性与公平性，揭示其潜在偏见。研究结果强调，在实体匹配中，尤其在阻断阶段，必须考虑公平性，以确保数据整合的公正结果。
原文摘要 · Abstract (English)
Entity Matching (EM) is crucial for identifying equivalent data entities across different sources, a task that becomes increasingly challenging with the growth and heterogeneity of data. Blocking techniques, which reduce the computational complexity of EM, play a vital role in making this process scalable. Despite advancements in blocking methods, the issue of fairness; where blocking may inadvertently favor certain demographic groups; has been largely overlooked. This study extends traditional blocking metrics to incorporate fairness, providing a framework for assessing bias in blocking techniques. Through experimental analysis, we evaluate the effectiveness and fairness of various blocking methods, offering insights into their potential biases. Our findings highlight the importance of considering fairness in EM, particularly in the blocking phase, to ensure equitable outcomes in data integration tasks.
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