  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.
