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.



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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