AutoBlock: A Hands-off Blocking Framework for Entity Matching

Wei Zhang, Hao Wei, Bunyamin Sisman, Xin Luna Dong, Christos Faloutsos, David Page

Abstract:

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, realworld datasets, especially when datasets are dirty and/or unstructured.