基于文本聚类和概念相似度的语义Web服务发现

发布时间：2019-06-11 18:06

【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。
[Abstract]:When semantic Web services discover services, they need to match the services in the registry in sequence, which wastes a lot of time on unrelated services, resulting in low efficiency of service discovery. In order to solve this problem, a new semantic Web service discovery method based on text clustering and conceptual similarity is proposed. The method is divided into two stages. In the first stage, according to the descriptive text information in the service source file, the services with consistent categories are grouped together. In this process, the vector space model is used to represent and process the text, and a multiple hybrid clustering algorithm MHC; is proposed on the basis of the predecessor. In the second stage, the functional attribute matching between services is carried out, and the semantic similarity between concepts is calculated by combining the depth, strength and inheritance of directed edges in ontology concept hierarchy tree. Finally, the experimental results show that the proposed method greatly improves the matching efficiency on the basis of taking into account the matching accuracy.
【作者单位】：江苏大学计算机科学与通信工程学院;
【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
【分类号】：TP393.09;TP391.1

【共引文献】