Markov logic networks

作     者：Richardson, M Domingos, P 

作者机构：Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：2006年第62卷第1-2期

页      面：107-136页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：statistical relational learning Markov networks Markov random fields log-linear models graphical models first-order logic satisfiability inductive logic programming knowledge-based model construction Markov chain Monte Carlo pseudo-likelihood link prediction 

摘      要：We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.