ShallowBlocker: Improving Set Similarity Joins for Blocking
This addresses the need for scalable and stable blocking in large-scale entity matching, though it appears incremental as it builds on classical methods with novel hybrid components.
The paper tackles the problem of manual engineering and instability in deep learning-based blocking for entity matching by proposing ShallowBlocker, a hands-off method using classical string similarity measures, which achieves state-of-the-art pair effectiveness in both unsupervised and supervised blocking.
Blocking is a crucial step in large-scale entity matching but often requires significant manual engineering from an expert for each new dataset. Recent work has show that deep learning is state-of-the-art and has great potential for achieving hands-off and accurate blocking compared to classical methods. However, in practice, such deep learning methods are often unstable, offers little interpretability, and require hyperparameter tuning and significant computational resources. In this paper, we propose a hands-off blocking method based on classical string similarity measures: ShallowBlocker. It uses a novel hybrid set similarity join combining absolute similarity, relative similarity, and local cardinality conditions with a new effective pre-candidate filter replacing size filter. We show that the method achieves state-of-the-art pair effectiveness on both unsupervised and supervised blocking in a scalable way.