ShallowBlocker: Improving Set Similarity Joins for Blocking
Nils Barlaug
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.