基于服务目标聚类的服务发现方法

Neng Zhang, Jian Wang, K. He, Zheng Li
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引用次数: 13

摘要

Web上发布的服务数量不断增加,这使得为用户发现相关服务变得困难。与由结构化WSDL文档描述的基于soap的服务不同,rest式服务(最流行的服务类型)主要使用短文本进行描述。现有服务注册中心采用的基于关键字的rest式服务发现技术,不足以根据用户需求获得准确的服务。此外,由于缺乏对预期服务功能的了解,用户指定完全反映其需求的查询仍然是一项困难的任务。在本文中,我们提出了一种面向目标的服务发现方法,该方法旨在为用户的功能目标获得准确的RESTful服务。该方法首先使用主题模型将现有服务分组到集群中。然后,它利用为服务训练的主题模型,对从服务文本描述中提取的服务目标进行聚类。基于服务目标集群,我们的方法可以通过推荐相似的服务目标来帮助用户优化他们的初始查询。最后,将用户选择的服务目标与现有服务目标进行匹配,得到相应的服务。在从ProgrammableWeb抓取的真实服务数据集上进行的实验表明了所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Approach of Service Discovery Based on Service Goal Clustering
The increasing amount of services published on the Web makes it difficult to discover relevant services for users. Unlike the SOAP-based services that are described by structural WSDL documents, RESTful services, the most popular type of services, are mainly described using short texts. The keyword-based discovery technology for RESTful services adopted by existing service registries is insufficient to obtain accurate services according to user requirements. Moreover, it remains a difficult task for users to specify queries that perfectly reflect their requirements due to the lack of knowledge of their expected service functionalities. In this paper, we propose a goal-oriented service discovery approach, which aims to obtain accurate RESTful services for user functional goals. The approach first groups existing services into clusters using topic models. It then clusters the service goals extracted from the textual descriptions of services by leveraging the topic model trained for services. Based on the service goal clusters, our approach can help users refine their initial queries by recommending similar service goals. Finally, relevant services are obtained by matching the service goals selected by users with those of existing services. Experiments conducted on a real-world service dataset crawled from ProgrammableWeb show the effectiveness of the proposed approach.
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