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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Markov Logic
verfasst von : Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla
Erschienen in: Probabilistic Inductive Logic Programming
Verlag: Springer Berlin Heidelberg
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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.