MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
This addresses the need for efficient and effective unsupervised entity matching across multiple tables in data management systems, representing an incremental advancement over traditional two-table methods.
The paper tackles the problem of unsupervised multi-table entity matching, which is more common in practice than the two-table setting, and proposes MultiEM, a pipeline solution that demonstrates superior effectiveness and efficiency on six real-world benchmark datasets.
Entity Matching (EM), which aims to identify all entity pairs referring to the same real-world entity from relational tables, is one of the most important tasks in real-world data management systems. Due to the labeling process of EM being extremely labor-intensive, unsupervised EM is more applicable than supervised EM in practical scenarios. Traditional unsupervised EM assumes that all entities come from two tables; however, it is more common to match entities from multiple tables in practical applications, that is, multi-table entity matching (multi-table EM). Unfortunately, effective and efficient unsupervised multi-table EM remains under-explored. To fill this gap, this paper formally studies the problem of unsupervised multi-table entity matching and proposes an effective and efficient solution, termed as MultiEM. MultiEM is a parallelable pipeline of enhanced entity representation, table-wise hierarchical merging, and density-based pruning. Extensive experimental results on six real-world benchmark datasets demonstrate the superiority of MultiEM in terms of effectiveness and efficiency.