基于多重关系主题模型的Web服务聚类方法  

Multi-Relational Topic Model-Based Approach for Web Services Clustering

作　　者：石敏 刘建勋 周栋 曹步清 文一凭 SHI Min;LIU Jian-Xun;ZHOU Dong;CAO Bu-Qing;WEN Yi-Ping(Key Laboratory of Knowledge Processing & Networked Manufacturing, Hunan University of Science & Technology, Xiangtan, Hunan 411201)

机构地区：[1]湖南科技大学知识处理与网络化制造湖南省普通高校重点实验室,湖南湘潭411201

出　　处：《计算机学报》2019年第4期820-836,共17页Chinese Journal of Computers

基　　金：国家自然科学基金(61872139;61876062;61572187);湖南省自然科学基金(2018JJ2139);湖南省教育厅创新平台开放基金项目(17K033)资助~~

摘　　要：如何有效地发现合适的Web服务是面向服务计算领域需要解决的核心问题之一.随着Internet上Web服务数量的不断增加,服务的自动发现面临着极大的挑战.将功能相似的Web服务进行聚类是一种有效的服务发现与服务管理方法.目前国内外主流的方法为挖掘Web服务的隐含功能语义信息,如使用LDA主题模型训练提取Web服务功能描述文档的主题信息,然后基于某种聚类算法如K-means将隐含主题分布相似的Web服务聚为一类.然而,Web服务的功能描述文档通常短小,目前大部分主题模型无法对短文本进行良好地建模,从而影响了Web服务聚类的效果.针对该问题,文中提出了一种考虑多重Web服务关系的概率主题模型MR-LDA,其可对Web服务之间相互组合的关系以及Web服务之间共享标签的关系进行建模,能有效提高Web服务聚类的精度.同时,基于该MR-LDA主题模型进一步提出了一种有效的Web服务聚类算法MR-LDA+,该算法首先利用上述多重Web服务关系信息对Web服务隐含主题分布概率矩阵进行修正,然后根据这些隐含主题对Web服务进行聚类.基于ProgrammableWeb收集的真实数据实验表明,文中所提出的方法明显优于其它Web服务聚类算法.Web service discovery is a significant and nontrivial task in the domain of Web service computing. With the rapid growth in the number of Web services on the Internet, e.g., an increasing number of enterprises tend to make public their software and other resources in the form of services within and outside the organizations, locating exactly the desired Web services is becoming increasingly hard for users. It has been shown that clustering Web services according to their functionalities is an efficient way to facilitate Web services discovery as well as services management. The clustering results can help us better understanding the more fine - granted categorically functional features of Web services, and meanwhile significantly reduce the searching space and retrieval time with respect to a given user query. Existing methods on this topic mainly focus on mining the semantic functional information of Web services, etc., adopting LDA to firstly elicit the functional semantics of Web services and then clustering Web services according to their topic distributions based on some clustering methods such as K -means. However, the natural description documents of Web services generally contain limit number of words. It is hard for most existing LDA-based methods to model short text documents, which may seriously degrade the Web service clustering accuracy. To narrow such negative effect, this paper aims to mitigate the data sparse issue by mining and leveraging some types of auxiliary information that is helpful to the service clustering problem. After a careful exploration of the Web service multi-relational network that is naturally established from users’ frequent behaviors (e.g., invoking and annotating) of using Web services, we found that the composition relationships between Web services and the annotation relationships between Web services sharing identical tags could be used to improve the semantics extraction and service clustering processes, i.e., services with annotation relationships tend to share simila

关 键 词：WEB服务 聚类 多重关系网络 先验知识 主题模型 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]