Abstract: Aiming at the problem existing in the traditional methods for Web service discovery, such as unobvious distinction of service matching degree and low precision of service discovery, an algorithm for spatial information service automatic discovery based on service cluster is proposed. This algorithm clusters the advertised spatial information service into some clusters and selects the most matching cluster through computing the similarity between the service request and each clustering center. The most matching spatial information service is determined according to the semantic similarity between service request and each matching spatial information service belongs to the most matching cluster. Experimental results show that this algorithm can quantify the Web service matching degree while improving the distinction, service discovery recall and efficiency.

Key words: spatial information service, clustering, semantic similarity, service cluster, service discovery, optimum matching cluster

摘要： 现有Web服务自动发现方法中存在服务匹配程度区分不明显、服务发现精度不高等问题。为此，提出一种基于服务簇的空间信息服务自动发现算法。对发布的空间信息服务进行聚类分析，计算服务请求与各服务簇中心的相似度，由此确定最优匹配簇，根据服务请求与最优匹配簇中服务的语义相似度，得出服务请求的最优匹配服务。实验结果表明，该算法在实现对Web服务匹配程度定量表示的同时，能有效提高匹配程度的区分度和服务发现的查全率和效率。

关键词: 空间信息服务, 聚类, 语义相似度, 服务簇, 服务发现, 最优匹配簇

CLC Number: 

• TP391