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中文摘要：
从数据中学习贝叶斯网络往往会因为搜索空间庞大而耗费大量时间．由于贝叶斯网络固有的因果语义，领域专家往往能够凭借自己的经验确定节点之间的因果关系．本文方法充分收集专家的意见，并利用证据理论进行综合，去除无意义的网络结构，然后利用常用的学习算法从数据中继续学习．这种融合知识和数据的贝叶斯网络构造方法利用专家知识来缩小学习算法的搜索空间，避免了盲目搜索，同时也避免了单个专家知识的主观性．实验表明该方法能够有效提高学习效率．
英文摘要：
Learning the structure of a Bayesian network from data may be time expensive due to huge search space. Because a Bayesian network contains causal semantics, experts can use their knowledge to confirm cause and effect among variables. In this paper, experts＇ opinions are collected and combined using Dempster-Shafer evidence theory. The network structures without semantics are eliminated, then learning network from data is continued. This method fuses expert knowledge which is used to reduce search space with data to construct a Bayesian network . It avoids the subjective bias of single expert. The experimental results show that this method can improve learning efficiency.
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