本文提出了一个以领域本体为驱动的网络搜索模型。以Internet上海量、多变的资源为背景，一方面通过语义推理，优化了网络搜索中存在的如查准率低等问题，另一方面利用本体库资源，在网络搜索过程中做问题激发，不仅增强了搜索的人性化，更提高了语义分析的效率。本文以股票领域本体为案例进行了验证，表明该模型对于资源类型丰富、结构复杂多变的大规模资源库，具有更灵活有效的语义搜索特征。

In this paper, an ontologybased Internet search model is designed. This model is proposed in the background of the Internet's mass, changeable resources.On the one hand, its semantic inference solves the problems, such as the low recall, which appears in the search; on the other hand, it makes good use of ontology resources to do query excitation that provides more userfriendly search and advances the efficiency of the ontological inference. This model is validated in the case of the stock domain ontology, and the results prove that the model has more flexible and effective semantic search characteristics for the complexstructured and typerich everchanging resources.