Summary of Evaluating Blocking Biases in Entity Matching, by Mohammad Hossein Moslemi et al.
Evaluating Blocking Biases in Entity Matching
by Mohammad Hossein Moslemi, Harini Balamurugan, Mostafa Milani
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a framework for assessing bias in Entity Matching (EM) blocking techniques, extending traditional metrics to incorporate fairness considerations. The authors evaluate various blocking methods through experimental analysis, highlighting the importance of considering fairness in EM, particularly in the blocking phase, to ensure equitable outcomes in data integration tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Entity Matching is important for identifying similar data entities across different sources. To make this process faster and more efficient, we use blocking techniques. However, these techniques can unintentionally favor certain groups of people. This paper looks at how to measure bias in these blocking methods so that they are fairer and more accurate. |
