中文摘要：

针对某些特定领域的建模中单一的语义检索条件无法得到理想的检索结果，提出了基于跨本体的语义相关度进行检索的算法。首先构建相关领域的本体，然后对已有实例进行分析，通过聚类算法找出模型本体间具有相关性的属性。再通过调查获取用户对实例的评价数据，对深度信念网络进行训练，求出本体间相关语义属性的相关度权值。最终对模型库中的模型计算与检索模型间的相关度作为检索条件，将大于一定阈值的模型作为检索结果。应用该算法，用户一般在检索首页可以找到较满意的模型，大大缩短了检索的时间。

英文摘要：

In some specific areas, a single condition of semantic retrieval cannot have the ideal results. A retrieval algorithm based on semantic correlation between different ontologies is presented. The algorithm firstly builds the domain ontology, and then analyzes the existing instances to find out the semantic correlation among ontologies by clustering algorithm. Besides, the evaluation data of the user of the instance which is obtained by the survey is used as the sample, with which the deep belief network （DBN） is trained to obtain the weights of correlation between semantics of different ontologies. Finally, the relevancy between the retrieved models and the model in database is computed and the models with higher relevancy are used as the retrieval results. With the retrieval algorithm, the designer can get more satisfactory model in retrieval homepage, which greatly shortens the retrieval time.