Markov logic

Authors:
Pedro Domingos;Stanley Kok;Daniel Lowd;Hoifung Poon;Matthew Richardson;Parag Singla
Affiliations:
Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA;Microsoft Research, Redmond, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA
Venue:
Probabilistic inductive logic programming
Year:
2008

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Abstract

Most real-world machine learning problems have both statistical and relational aspects. Thus learners need representations that combine probability and relational logic. Markov logic accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the conjugate gradient algorithm, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to problems in entity resolution, link prediction, information extraction and others, and is the basis of the open-source Alchemy system.