A Posterior-Based Method for Markov Logic Networks Parameters Learning

摘要：

The past few years have witnessed a significant development in statistical relational learning (SRL). Markov logic network (MLN), one of SRL methods, is a first-order knowledge base with a weight attached to each formula, and could be viewed as a template for ground Markov logic networks. In this paper, a posterior-based parameters learning approach for Markov logic networks, maximum pseudo-posterior estimation is proposed. Mean Gaussian distribution is used as prior of each weight, likelihood is replaced by pseudo-likelihood, and the pseudo-posterior distribution is maximized to learn the weights. Experiments show maximum pseudo-posterior estimation could learn MLNs model effectively, which performs inference better compared to maximum pseudo-likelihood estimation.

关键词： Statistical Relational Learning First-Order Logic Markov Networks Machine Learning Markov Logic Networks.

作者: Shuyang Sun Jianzhong Chen Dayou Liu Chengmin Sun

作者单位: College of Computer Science and Technology, Jilin University, 130012 Changchun, China;Key Laboratory Computational Bioinformatics Laboratory, Department of Computing, Imperial College London, SW7 2BZ L

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 529-534

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）