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Markov Logic Networks (MLNs) have emerged as a powerful framework thatcombines statistical and logical reasoning; they have been applied to many dataintensive problems including information extraction, entity resolution, andtext mining. Current implementations of MLNs do not scale to large real-worlddata sets, which is preventing their wide-spread adoption. We present Tuffythat achieves scalability via three novel contributions: (1) a bottom-upapproach to grounding that allows us to leverage the full power of therelational optimizer, (2) a novel hybrid architecture that allows us to performAI-style local search efficiently using an RDBMS, and (3) a theoretical insightthat shows when one can (exponentially) improve the efficiency of stochasticlocal search. We leverage (3) to build novel partitioning, loading, andparallel algorithms. We show that our approach outperforms state-of-the-artimplementations in both quality and speed on several publicly availabledatasets.

Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS