SlimShot: In-Database Probabilistic Inference for Knowledge Bases

4 years 6 months ago
SlimShot: In-Database Probabilistic Inference for Knowledge Bases
Increasingly large Knowledge Bases are being created, by crawling the Web or other corpora of documents, and by extracting facts and relations using machine learning techniques. To manage the uncertainty in the data, these KBs rely on probabilistic engines based on Markov Logic Networks (MLN), for which probabilistic inference remains a major challenge. Today’s state of the art systems use variants of MCMC, which have no theoretical error guarantees, and, as we show, suffer from poor performance in practice. In this paper we describe SlimShot (Scalable Lifted Inference and Monte Carlo Sampling Hybrid Optimization Technique), a probabilistic inference engine for knowledge bases. SlimShot converts the MLN to a tuple-independent probabilistic database, then uses a simple Monte Carlo-based inference, with three key enhancements: (1) it combines sampling with safe query evaluation, (2) it estimates a conditional probability by jointly computing the numerator and denominator, and (3) it ...
Eric Gribkoff, Dan Suciu
Added 09 Apr 2016
Updated 09 Apr 2016
Type Journal
Year 2016
Authors Eric Gribkoff, Dan Suciu
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