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Entity Resolution suffers from quadratic time complexity. To increase itstime efficiency, three kinds of filtering techniques are typically used forrestricting its search space: (i) blocking workflows, which group togetherentity profiles with identical or similar signatures, (ii) string similarityjoin algorithms, which quickly detect entities more similar than a threshold,and (iii) nearest-neighbor methods, which convert every entity profile into avector and quickly detect the closest entities according to the specifieddistance function. Numerous methods have been proposed for each type, but theliterature lacks a comparative analysis of their relative performance. As weshow in this work, this is a non-trivial task, due to the significant impact ofconfiguration parameters on the performance of each filtering technique. Weperform the first systematic experimental study that investigates the relativeperformance of the main methods per type over 10 real-world datasets. For eachmethod, we consider a plethora of parameter configurations, optimizing it withrespect to recall and precision. For each dataset, we consider bothschema-agnostic and schema-based settings. The experimental results providenovel insights into the effectiveness and time efficiency of the consideredtechniques, demonstrating the superiority of blocking workflows and stringsimilarity joins.

How to reduce the search space of Entity Resolution: with Blocking or Nearest Neighbor search?