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

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 large 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 users to leverage the full power of the relational optimizer, a novel hybrid architecture that allows users 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.

Provided by: VLD Digital Topic: Data Management Date Added: Sep 2011 Format: PDF

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