Scalable learning and inference in Markov logic networks
文献类型：期刊论文
| 作者 | Sun, Zhengya ; Zhao, Yangyang; Wei, Zhuoyu; Zhang, Wensheng; Wang, Jue
|
| 刊名 | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
 |
| 出版日期 | 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) |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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