CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
Scalable learning and inference in Markov logic networks
Sun, Zhengya; Zhao, Yangyang; Wei, Zhuoyu; Zhang, Wensheng; Wang, Jue
Source PublicationINTERNATIONAL JOURNAL OF APPROXIMATE REASONING
2017-03-01
Volume2017Issue:82Pages:39-55
SubtypeArticle
AbstractMarkov 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.
KeywordMarkov Logic Networks Structure Learning Probabilistic Inference Large Scale Machine Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.ijar.2016.12.003
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61303179)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000393733000003
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14386
Collection精密感知与控制研究中心_人工智能与机器学习
AffiliationChinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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