Large-Scale Collective Entity Matching

Details Large-Scale Collective Entity Matching Large-Scale Collective Entity Matching

2011-03-12

arxiv.org/abs/1103.2410v1

Proceedings of the VLDB Endowment (PVLDB), Vol. 4, No. 4, pp. 208-218 (2011)

Computer Science

Databases

VLDB2011

Scientific paper

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.

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