Generalized Supervised Meta-blocking (technical report)

TL;DR

This paper introduces a generalized supervised meta-blocking method for entity resolution that leverages probabilistic classifiers and feature-based weighting schemes to improve candidate pair filtering, achieving high effectiveness across benchmarks.

Contribution

It proposes a flexible, classifier-based meta-blocking framework that enhances blocking efficiency by integrating multiple scoring features and optimizing pruning strategies.

Findings

Best pruning algorithms identified

Optimal feature sets determined

Minimal training set size established

Abstract

Entity Resolution constitutes a core data integration task that relies on Blocking in order to tame its quadratic time complexity. Schema-agnostic blocking achieves very high recall, requires no domain knowledge and applies to data of any structuredness and schema heterogeneity. This comes at the cost of many irrelevant candidate pairs (i.e., comparisons), which can be significantly reduced through Meta-blocking techniques, i.e., techniques that leverage the co-occurrence patterns of entities inside the blocks: first, a weighting scheme assigns a score to every pair of candidate entities in proportion to the likelihood that they are matching and then, a pruning algorithm discards the pairs with the lowest scores. Supervised Meta-blocking goes beyond this approach by combining multiple scores per comparison into a feature vector that is fed to a binary classifier. By using probabilistic classifiers, Generalized Supervised Meta-blocking associates every pair of candidates with a score that can be used by any pruning algorithm. For higher effectiveness, new weighting schemes are examined as features. Through an extensive experimental analysis, we identify the best pruning algorithms, their optimal sets of features as well as the minimum possible size of the training set. The resulting approaches achieve excellent performance across several established benchmark datasets.