Exploiting block co-occurrence to control block sizes for entity resolution

The problem of identifying duplicated entities in a dataset has gained increasing importance during the last decades. Due to the large size of the datasets, this problem can be very costly to be solved due to its intrinsic quadratic complexity. Both researchers and practitioners have developed a variety of techniques aiming to speed up a solution to this problem. One of these techniques is called blocking, an indexing technique that splits the dataset into a set of blocks, such that each block contains entities that share a common property evaluated by a blocking key function. In order to improve the efficacy of the blocking technique, multiple blocking keys may be used, and thus, a set of blocking results is generated. In this paper, we investigate how to control the size of the blocks generated by the use of multiple blocking keys and maintain reasonable quality results, which is measured by the quality of the produced blocks. By controlling the size of the blocks, we can reduce the overall cost of solving an entity resolution problem and facilitate the execution of a variety of tasks (e.g., real-time and privacy-preserving entity resolution). For doing so, we propose many heuristics which exploit the co-occurrence of entities among the generated blocks for pruning, splitting and merging blocks. The experimental results we carry out using four datasets confirm the adequacy of the proposed heuristics for generating block sizes within a predefined range threshold as well as maintaining reasonable blocking quality results.