Networking

Tuffy: Scaling Up Statistical Inference in Markov Logic Networks

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Executive Summary

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to real-world data sets, which is preventing their wide-spread adoption. The authors present Tuffy that achieves scalability via three novel contributions: A bottom-up approach to grounding that allows one to leverage the full power of the relational optimizer, a novel hybrid architecture that allows one to perform AI-style local search efficiently using an RDBMS, and a theoretical insight that shows when one can (Exponentially) improve the efficiency of stochastic local search.

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