Scalable learning and inference in Markov logic networks

doi:10.1016/j.ijar.2016.12.003

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	Scalable learning and inference in Markov logic networks
	Sun, Zhengya; Zhao, Yangyang; Wei, Zhuoyu; Zhang, Wensheng; Wang, Jue
刊名	INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
	2017-03-01
卷号	2017 期号:82 页码:39-55
关键词	Markov Logic Networks Structure Learning Probabilistic Inference Large Scale Machine Learning
DOI	10.1016/j.ijar.2016.12.003
文献子类	Article
英文摘要	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.
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000393733000003
资助机构	National Natural Science Foundation of China(61303179)
内容类型	期刊论文
源URL	[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.