融合功能语义与服务协作关系的Web服务聚类方法  

Method to cluster Web services by integrating functional semantics and service collaboration

作　　者：王琨 胡强[1] 王华东[1] 杜军威[1] 潘国庆 WANG Kun;HU Qiang;WANG Huadong;DU Junwei;PAN Guogqing(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,山东青岛266061

出　　处：《计算机集成制造系统》2023年第4期1336-1345,共10页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(61973180);山东省自然科学基金资助项目(ZR2019MF033,ZR2021MF092);山东省重点研发计划软科学资助项目(2021RKY02037)。

摘　　要：当前Web服务聚类方法主要关注服务功能描述语义信息,缺乏对服务之间协作关系的考量。为进一步提高服务聚类质量,提出一种融合功能语义与服务协作关系的服务聚类方法。首先通过融合标签向量和服务描述文本向量生成高质量功能语义表达的服务功能特征向量;然后建立服务协作网络,构建加权GraphSAGE模型,在服务协作网络上实现邻域服务结点的特征聚合,获得融入协作关系的服务表征向量;最后采用K-means++算法进行聚类。实验表明,相比其他常用模型,所提方法所生成的服务功能向量提高了聚类质量,协作特征的融入进一步提升了聚类效果。Current Web service clustering methods mainly focus on the semantic information of service function description,but lack of consideration of service collaboration.To further improve service clustering quality,a service clustering method integrating functional semantics and service collaboration was proposed.The service function feature vectors with high-quality function semantic expression were generated by fusing tag vectors and service description text vectors.Then the service collaboration network was established.A weighted GraphSAGE model was designed to aggregate the features of neighborhood service nodes on the service collaboration network.Service representation vector was obtained by integrating the collaboration relationship into service function feature vector.Finally,the K-means algorithm was used for clustering.Experiments showed that the service function vector generated by this method significantly improved the clustering quality by comparing with the traditional models.Moreover,the integration of collaborative features further improved the effect of service clustering.

关 键 词：WEB服务 服务聚类 功能语义 服务协作 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]