Scalable learning and inference in Markov logic networks

doi:10.1016/j.ijar.2016.12.003

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Scalable learning and inference in Markov logic networks
	Sun, Zhengya; Zhao, Yangyang; Wei, Zhuoyu; Zhang, Wensheng; Wang, Jue
发表期刊	INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
	2017-03-01
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
DOI	10.1016/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	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14386
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
作者单位	Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
第一作者单位	中国科学院自动化研究所
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.