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| 一种基于近似因子的在线概率知识库推理方法 |
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王艳艳1,2, 陈群1,2, 钟评1,2, 李战怀1,2
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1.西北工业大学 计算机学院, 陕西 西安 710129;2.大数据存储与管理工业和信息化部重点实验室(西北工业大学), 陕西 西安 710129
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| 摘要: |
| 概率知识库中的推理技术是近年来的研究热点.目前，大多数系统的推理主要基于批处理的方式实现，并不适用于在线查询场景.对此，提出了一种基于近似因子的在线概率知识库推理方法.它可以重复利用已推断结果计算查询变量的边缘概率.该算法首先提取查询变量的子图（含已推断变量）；然后，在此子图上添加近似因子，以模拟子图外其余变量的影响；最后，采用团树算法推断查询变量的边缘概率.实验结果表明：相对于已有算法，该算法可在时间和精度上取得较好的权衡. |
| 关键词: 概率知识库 在线推理 近似因子 马尔可夫逻辑网 |
| DOI：10.13328/j.cnki.jos.005388 |
| 分类号:TP181 |
| 基金项目:国家重点研发计划（2016YFB1000703）；国家自然科学基金（61332006，61732014，61672432，61472321，61502390） |
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| Online Inference Based on Approximate Factors for Probabilistic Knowledge Bases |
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WANG Yan-Yan1,2, CHEN Qun1,2, ZHONG Ping1,2, LI Zhan-Huai1,2
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1.School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China;2.Key Laboratory of Big Data Storage and Management(Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710129, China
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| Abstract: |
| The inference techniques for probabilistic knowledge bases have recently attracted significant attentions. In most off-the-shelf existing systems, the inference is mainly implemented based on batch processing and thus not suited for online querying. This paper proposes an online inference approach based on approximate factors for probabilistic knowledge bases, so as to provide a way to reuse those inferred results to calculate the marginal probability for the query variable. In this approach, a subgraph is extracted first, taking the query variable as center; then some approximate factors are attached to simulate the influences from the variables outside the subgraph; and finally, the marginal probability of the query variable is calculated by the clique tree algorithm. Experiments show that compared with existing algorithms, the presented approach can achieve a better tradeoff between accuracy and time. |
| Key words: probabilistic knowledge base online inference approximate factor Markov logic network |
