基于粗糙集的定性概率网整合方法

由于子定性概率网(QPN)仅局限于表示子领域知识，为构建一个较大QPN进行知识的全面表示，基于粗糙集理论，提出了一种具有不同节点的多个子QPN整合方法。在QPN中，可将单个变量或多个变量的组合看做粗糙集中的一个属性。当多个QPN整合时，首先合并多个子QPN结构;然后，在保证不出现环路的情况下，根据粗糙集的属性间的依赖度向合并的QPN中添加有向边及其定性符号;接着，再根据属性间相对必要性来删除具有多个父节点的属性所不必要的冗余边，从而整合出较大QPN。最后，实验验证了该整合方法的可行性和有效性。

Integration method of qualitative probabilistic networks based on rough sets

Qualitative Probabilistic Network (QPN) is a powerful knowledge representation tool. However, sub-QPN can only represent sub-domain knowledge. To build a large QPN to represent the whole domain knowledge, an integration method of multiple sub-QPNs that have different nodes was proposed based on rough sets. Specifically, a single variable or a combination of multiple variables in a QPN could be regarded as an attribute in rough sets. First, multiple sub-QPNs were combined into an initial integrated QPN during integrating, then the directed edges and qualitative signs were added into the QPN according to attribute dependency degree, and then some unnecessary edges of which child node had multiple parent nodes could be deleted according to relative necessity of attribute. Thus, a large integrated QPN would be obtained to represent the whole domain knowledge. Finally, the experimental results illustrate that the integration method is feasible and effective.