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Home > Archive>Volume 29, Issue 2, 2018 >383-395. DOI:10.13328/j.cnki.jos.005388
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Online Inference Based on Approximate Factors for Probabilistic Knowledge Bases
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TP181

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National Key Research and Development Program of China (2016YFB1000703); National Natural Science Foundation of China (61332006, 61732014, 61672432, 61472321, 61502390)

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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.

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王艳艳,陈群,钟评,李战怀.一种基于近似因子的在线概率知识库推理方法.软件学报,2018,29(2):383-395

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History
  • Received:April 10,2017
  • Revised:May 18,2017
  • Online: November 29,2017
Links:Institute of Software, CASChina Computer FederationCASNSFC
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