【摘要】 针对语义网信息检索中存在的问题,提出了一个基于语义索引词的语义网信息检索模型SIR(semantic information retrieval).其核心思想是将领域本体转换成全局本体,并从全局本体中提取语义索引词.通过语义索引词进行语义推理,可得概念的逻辑视图.SIR通过语义索引词间的语义关系对网络资源进行检索,解决了在传统的基于关键字的信息检索中只能从句法上对关键字进行分析,无法根据信息资源中的语义关系进行检索的问题.最后分析了SIR的可用性,证明了SIR可极大地提高语义网上信息检索的查全率和查准率.更多还原

【Abstract】 In order to solve the problem of information retrieval on the semantic web,a new semantic information retrieval (SIR) model for searching ontologies on the semantic web is proposed.First,SIR transformed domain ontologies into global ontologies.Then semantic index terms were extracted from these global ontologies.Based on semantic index terms, logical inferences can be performed and the logical views of the concept can be obtained.These logical views represent the expanded meaning of the concept.Using logical views,SIR can perform the information retrieval and inferences based on the semantic relationships in the documents,not only on the syntactic analysis of the documents.SIR can significantly enhance the recall and precision of the information retrieval by the semantic inference.Finally,the practicability of the SIR model is analyzed.更多还原