摘要
【目的】因用户可应用的云服务数量呈指数级增长,进而产生云服务发现和选择相关问题。【方法】语义检索技术采取信息检索、语义分析和信息融合等方法提高云服务检索效率,并结合本体技术保持检索内容的准确性和一致性,实现用户基于关键词发现和选择云服务。【结果】实现对云服务的语义表示和标注,根据标注结果进行术语抽取,采用向量值创建语义索引,利用语义搜索引擎计算索引与关键字之间的相似度,得出关键字与文本之间的相似度。【局限】语义检索系统中部分模块涉及的相关算法仍有待深入研究,本文从整体性研究语义检索系统,各模块仅应用基本算法,没有涉及算法改良。【结论】经过实证评估分析,本体技术应用于语义检索系统能够有效提高云服务检索精准度,特别适用于非结构化信息检索,但需要保持本体与语义变化的一致性。
[Objective] As the number of available cloud services increases exponentially, the problem of cloud service discovery and selection arises. [Methods] Semantic retrieval technology in use of information retrieval, semantic analysis and information fusion can improve retrieval efficiency. Combined with Ontology technology can ensure search processes accuracy and consistency, and realize cloud service discovery and selection. [Results] This paper can semantically represent and semantically annotate cloud services. According to extracting semantically annotate terms, it applies vector value to create semantic indexing. Using semantic search engine calculate vector space value between query sentence and index data, and obtain documents similarity. [Limitations] Relevant algorithms involved in some semantic retrieval system are still in development. This paper researches semantic retrieval system as a whole, every module just applies these basic algorithms, algorithm improvement is not involved. [Conclusions] Empirical research proves Ontology technology applied in semantic retrieval system achieves good effects. Especially it is suitable for retrieval of unstructured information, when changes between Ontology and semantic need to keep consistency.
出处
《现代图书情报技术》
CSSCI
北大核心
2014年第12期27-35,共9页
New Technology of Library and Information Service
基金
黑龙江省教育厅人文社会科学面上项目"黑龙江省突发事件应急管理体系模型构建研究"(项目编号:11552204)的研究成果之一
关键词
本体
语义标注
术语抽取
语义检索
云服务
Ontology Semantic annotation Term extraction Semantic retrieval Cloud services
