Large-Scale Collective Entity Matching
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, the authors propose a principled framework to scale any generic EM algorithm. Their 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. They prove formal properties of their framework and experimentally demonstrate the effectiveness of their approach in scaling EM algorithms.