2009 LearningMarkovLogicNetStrucure

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Subject Headings: Markov Logic Network, Hypergraph.

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Abstract

Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning MLN structure from a relational database involves learning the clauses and weights. The state-of-the-art MLN structure learners all involve some element of greedily generating candidate clauses, and are susceptible to local optima. To address this problem, we present an approach that directly utilizes the data in constructing candidates. A relational database can be viewed as a hypergraph with constants as nodes and relations as hyperedges. We find paths of true ground atoms in the hypergraph that are connected via their arguments. To make this tractable (there are exponentially many paths in the hypergraph), we lift the hypergraph by jointly clustering the constants to form higher-level concepts, and find paths in it. We variabilize the ground atoms in each path, and use them to form clauses, which are evaluated using a pseudo-likelihood measure. In our experiments on three real-world datasets, we find that our algorithm outperforms the state-of-the-art approaches.,


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 LearningMarkovLogicNetStrucureStanley Kok
Pedro Domingos
Learning Markov Logic Network Structure via Hypergraph LiftingProceedings of ICML 2009http://conflate.net/icml/paper/2009/5762009