Markov Logic: A Unifying Framework for Statistical Relational Learning

Pedro Domingos and Matthew Richardson


Interest in statistical relational learning (SRL) has grown rapidly in recent years. Several key SRL tasks have been identified, and a large number of approaches have been proposed. Increasingly, a unifying framework is needed to facilitate transfer of knowledge across tasks and approaches, to compare approaches, and to help bring structure to the field. We propose Markov logic as such a framework. Syntactically, Markov logic is indistinguishable from first-order logic, except that each formula has a weight attached. Semantically, a set of Markov logic formulas represents a probability distribution over possible worlds, in the form of a log-linear model with one feature per grounding of a formula in the set, with the corresponding weight. We show how approaches like probabilistic relational models, knowledge- based model construction and stochastic logic programs can be mapped into Markov logic. We also show how tasks like collective classification, link prediction, link- based clustering, social network modeling, and object identification can be concisely formulated in Markov logic. Finally, we develop learning and inference algorithms for Markov logic, and report experimental results on a link prediction task.


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