Markov Logic
International Workshop on Inductive Logic Programming(2008)
摘要
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.
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关键词
conjugate gradient algorithm,entity resolution,inductive logic programming,relational aspect,relational logic,Markov network,Markov logic draw,Markov chain,Markov logic,Monte Carlo
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