Abstract: To overcome shortcomings present in the SEM in the presence of missing values data sets such as lower precision and slow convergent speed，KB-SEM is proposed by introducing domain knowledge.A tabu list derived from collecting domain expert knowledge synthesized by using D-S evidence theory is embedded in the SEM to constrain and guide the searching path，and to decrease the searching space.Experimental result shows that KB-SEM can improve the precision and the speed effectively，and to some extent avoid subjective bias and disturbance of noise in the data sets.

Key words: Bayesian Networks（BNs）, domain knowledge, missing data, KB-SEM

摘要： 针对SEM算法在缺省数据学习中存在精度偏低和收敛速度缓慢的问题，通过将领域知识引入到SEM算法中，提出了KB-SEM算法，该算法首先用D-S证据理论综合领域知识，然后将采集的知识以禁忌表的方式嵌入SEM中来限制和引导算法的搜索路径，缩小算法的搜索空间。实验表明，KB-SEM算法能有效地提高算法的学习精度和时间性能，且能在一定程度上避免主观偏见和数据噪音的干扰。

关键词: 贝叶斯网络, 领域知识, 缺省数据, KB-SEM