Efficiently discovering relevant Web services with respect to a specific user query has become a growing challenge owing to the incredible growth in the field of web technologies. In previous works, different clustering models have been used to address these issues. But, most of the traditional clustering techniques are computationally intensive and fail to address all the problems involved. Also, the current standards fail to incorporate the semantic relatedness of Web services during clustering and retrieval resulting in decreased performance. In this paper, we propose a framework for web services retrieval that uses a bottom-up, decentralized and self organising approach to cluster available services. It also provides online, dynamic computation of clusters thus overcoming the drawbacks of traditional clustering methods. We also use the semantic similarity between Web services for the clustering process to enhance the precision and lower the recall.
翻译:由于网络技术领域的惊人增长,有效发现与特定用户查询有关的相关网络服务已成为一项日益严峻的挑战。在以往的著作中,曾使用不同的集群模型来解决这些问题。但是,大多数传统集群技术都是在计算上密集的,未能解决所有所涉及的问题。此外,目前的标准没有在集群和检索过程中纳入网络服务的语义关联性,导致业绩下降。在本文件中,我们提出了一个网络服务检索框架,对集群现有服务采用自下而上、分散和自我组织的方法。它还提供在线、动态的集群计算,从而克服传统集群方法的缺陷。我们还利用网络服务在集群过程中的语义相似性来提高分类的精确度和降低回收率。