  There have been several recent advancements in Machine Learning community on
the Entity Matching (EM) problem. However, their lack of scalability has
prevented them from being applied in practical settings on large real-life
datasets. Towards this end, we propose a principled framework to scale any
generic EM algorithm. Our technique consists of running multiple instances of
the EM algorithm on small neighborhoods of the data and passing messages across
neighborhoods to construct a global solution. We prove formal properties of our
framework and experimentally demonstrate the effectiveness of our approach in
scaling EM algorithms.
