Entity Resolution matches mentions of the same entity. Being an expensive task for large data, its performance can be improved by blocking, i.e., grouping similar entities and comparing only entities in the same group. Blocking improves the run-time of Entity Resolution, but it still involves unnecessary comparisons that limit its performance. Meta-blocking is the process of restructuring a block collection in order to prune such comparisons. Existing unsupervised meta-blocking methods use simple pruning rules, which offer a rather coarse-grained filtering technique that can be conservative (i.e., keeping too many unnecessary comparisons) or aggressive (i.e., pruning good comparisons). In this work, we introduce supervised meta-blocking techniques that learn classification models for distinguishing promising comparisons. For this task, we propose a small set of generic features that combine a low extraction cost with high discriminatory power. We show that supervised meta-blocking can achieve high performance with small training sets that can be manually created. We analytically compare our supervised approaches with baseline and competitor methods over 10 large-scale datasets, both real and synthetic.
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