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CORR
2011
Springer

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

8 years 7 days ago
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 widespread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to leverage the full power of the relational optimizer, (2) a novel hybrid architecture that allows us to perform AI-style local search efficiently using an RDBMS, and (3) a theoretical insight that shows when one can (exponentially) improve the efficiency of stochastic local search. We leverage (3) to build novel partitioning, loading, and parallel algorithms. We show that our approach outperforms state-of-the-art implementations in both quality and speed on several publicly available datasets.
Feng Niu, Christopher Ré, AnHai Doan, Jude
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2011
Where CORR
Authors Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik
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