Scalable learning and inference in Markov logic networks
Sun, Zhengya; Zhao, Yangyang; Wei, Zhuoyu; Zhang, Wensheng; Wang, Jue
2017-03-01
发表期刊INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷号2017期号:82页码:39-55
文章类型Article
摘要Markov logic networks (MLNs) have emerged as a powerful representation that incorporates first-order logic and probabilistic graphical models. They have shown very good results in many problem domains. However, current implementations of MLNs do not scale well due to the large search space and the intractable clause groundings, which is preventing their widespread adoption. In this paper, we propose a general framework named Ground Network Sampling (GNS) for scaling up MLN learning and inference. GNS offers a new instantiation perspective by encoding ground substitutions as simple paths in the Herbrand universe, which uses the interactions existing among the objects to constrain the search space. To further make this search tractable for large scale problems, GNS integrates random walks and subgraph pattern mining, gradually building up a representative subset of simple paths. When inference is concerned, a template network is introduced to quickly locate promising paths that can ground given logical statements. The resulting sampled paths are then transformed into ground clauses, which can be used for clause creation and probabilistic inference. The experiments on several real-world datasets demonstrate that our approach offers better scalability while maintaining comparable or better predictive performance compared to state-of-the-art MLN techniques. (C) 2016 Elsevier Inc. All rights reserved.
关键词Markov Logic Networks Structure Learning Probabilistic Inference Large Scale Machine Learning
WOS标题词Science & Technology ; Technology
DOI10.1016/j.ijar.2016.12.003
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61303179)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000393733000003
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14386
专题精密感知与控制研究中心_人工智能与机器学习
作者单位Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
推荐引用方式
GB/T 7714
Sun, Zhengya,Zhao, Yangyang,Wei, Zhuoyu,et al. Scalable learning and inference in Markov logic networks[J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING,2017,2017(82):39-55.
APA Sun, Zhengya,Zhao, Yangyang,Wei, Zhuoyu,Zhang, Wensheng,&Wang, Jue.(2017).Scalable learning and inference in Markov logic networks.INTERNATIONAL JOURNAL OF APPROXIMATE REASONING,2017(82),39-55.
MLA Sun, Zhengya,et al."Scalable learning and inference in Markov logic networks".INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2017.82(2017):39-55.
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