摘要
语义Web服务在进行服务发现时,需要按顺序依次匹配注册库中的服务,这将大量时间浪费在不相干的服务上,从而造成服务发现效率低下。针对该问题,提出了一种新的基于文本聚类和概念相似度的语义Web服务发现方法。该方法主要分为两个阶段,第一阶段根据服务源文件中的描述性文本信息将类别一致的服务聚类到一起,在此过程中利用了向量空间模型对文本进行表示和处理,并在前人的基础上提出了一种多重混合聚类算法MHC;第二阶段进行服务间的功能属性匹配,结合本体概念层次树中有向边的深度、强度以及概念的继承度等因素计算概念间的语义相似度。最后,实验结果表明,提出的方法在兼顾匹配准确率的基础上,大大提高了匹配效率。
Semantic Web Services need to match services in the registry in succession in the discovery of services,which wastes a lot of time on irrelevant services, and reduces efficiency of discovery. Thus, a new discovery method of semantic Web service based on text clustering and similarity of concepts was proposed which can be divided into two phases： in the first phase, services of identical category are clustered according to descriptive texts in the service source file when texts are expressed and processed by vector space modal（VSM）and a multiple hybrid clustering algorithm MHC was proposed in the second phase, functions and properties between services are matched and semantic similarity between concepts is calculated combined with factors such as depth, strength and inheritance of directed edge in the hierarchical tree of ontology concepts. Finally, the experimental result shows that the method proposed in the article improves the matching efficiency greatly based on accurate rate.
出处
《计算机科学》
CSCD
北大核心
2013年第11期211-214,共4页
Computer Science
基金
江苏大学高级专业人才科研启动基金项目(10JDG063)
江苏省社会发展计划(BS2001 046)
江苏省高校自然科学研究计划(03kjd520075)资助
关键词
语义WEB服务
服务发现
文本聚类
本体
语义相似度
Semantic Web services, Web service discovery, Text clustering, Ontology, Semantic similarity
