  In this paper, we present an end-to-end multi-source Entity Matching problem,
which we call entity group matching, where the goal is to assign to the same
group, records originating from multiple data sources but representing the same
real-world entity. We focus on the effects of transitively matched records,
i.e. the records connected by paths in the graph G = (V,E) whose nodes and
edges represent the records and whether they are a match or not. We present a
real-world instance of this problem, where the challenge is to match records of
companies and financial securities originating from different data providers.
We also introduce two new multi-source benchmark datasets that present similar
matching challenges as real-world records. A distinctive characteristic of
these records is that they are regularly updated following real-world events,
but updates are not applied uniformly across data sources. This phenomenon
makes the matching of certain groups of records only possible through the use
of transitive information.
  In our experiments, we illustrate how considering transitively matched
records is challenging since a limited amount of false positive pairwise match
predictions can throw off the group assignment of large quantities of records.
Thus, we propose GraLMatch, a method that can partially detect and remove false
positive pairwise predictions through graph-based properties. Finally, we
showcase how fine-tuning a Transformer-based model (DistilBERT) on a reduced
number of labeled samples yields a better final entity group matching than
training on more samples and/or incorporating fine-tuning optimizations,
illustrating how precision becomes the deciding factor in the entity group
matching of large volumes of records.
