AutoBlock: A Hands-off Blocking Framework for Entity Matching
This addresses the labor-intensive blocking step in entity matching for data integration tasks, offering an automated solution that is scalable and effective.
The paper tackles the problem of reducing human effort in blocking for entity matching by proposing AutoBlock, a framework that automates data cleaning and blocking key tuning, and it outperforms baselines on large-scale, real-world datasets, especially with dirty or unstructured data.
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity. Virtually every entity matching task on large datasets requires blocking, a step that reduces the number of record pairs to be matched. However, most of the traditional blocking methods are learning-free and key-based, and their successes are largely built on laborious human effort in cleaning data and designing blocking keys. In this paper, we propose AutoBlock, a novel hands-off blocking framework for entity matching, based on similarity-preserving representation learning and nearest neighbor search. Our contributions include: (a) Automation: AutoBlock frees users from laborious data cleaning and blocking key tuning. (b) Scalability: AutoBlock has a sub-quadratic total time complexity and can be easily deployed for millions of records. (c) Effectiveness: AutoBlock outperforms a wide range of competitive baselines on multiple large-scale, real-world datasets, especially when datasets are dirty and/or unstructured.