  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.
