模糊知识、随机知识、不完全知识等典型不确定知识普遍存在，现有语义网技术不具备处理这些知识的能力。课题将解决语义网中典型不确定知识表示的关键理论和技术问题，为典型不确定知识的信息检索奠定基础，研究内容包括：(1)典型不确定知识的本体表示模型研究。首先研究使用云模型、粗糙集和概率论等理论表示典型不确定知识的方法，扩展描述逻辑SHOIN(D)和OWL DL语言构建不确定本体模型，然后研究不确定SHOIN(D)的Tableau算法和其转换为经典SHOIN(D)的算法，进而构建不确定本体推理算法；(2)不确定本体融合模型研究。首先研究具有典型不确定知识特征的本体元素相似度算法，进而构建基于分类或权值计算的映射算法，然后依据映射结果，研究不确定本体元素融合算法、本体结构和语义冲突处理规则，进而构建不确定本体融合算法；(3)搭建不确定语义网原型系统。课题的研究将对语义网中进行全方位知识检索具有重要意义。

Widespread representative uncertainty knowledge can not be dealt with by existing Semantic Web technologies such as fuzzy knowledge,random knowledge and incomplete knowlege. For soloving the critical theoretical and technical issues of the representative uncertain knowledge representation,and laying the foundation for information retrieval of representative uncertainty knowledge,the topics of the study will focus on the following basic theories and algorithms:(1)study of ontology representation modle of representative uncertain knowledge.Firstly these theories of cloud modle,rough sets and probability will be used to represent uncertain knowledge, and description logic SHOIN(D) and OWL DL language will be extended to construct uncertainty ontology model; then,tableau algorithm of the uncertainty SHOIN(D) and the algorithm of uncertain SHOIN(D) being turned to classical SHOIN(D) will be researched to construct the reasoning algorithm of uncertain ontology; (2) study of uncertain ontology integration. Firstly similarity algorithm between ontology elements based on fuzziness, randomicity and uncompleteness should be studied to construct ontology mapping algorithm based on classification or weight value calculation, and secondly according to the results of mapping,integration algorithm of uncertainty ontology elements and rules of structural and semantic conflict management will be researched to build the integration model;(3)On the basis of above studies, uncertain semantic Web prototype system will be able to be structured. The subject will solve the problems about representation and application of uncertain knowledge in Semantic Web, and it will bring the significance for all-dimensional knowledge retrieval in Semantic Web.