摘要
Web服务数量的激增对服务发现提出了更高的要求,服务聚类是促进服务发现的一种重要技术.但是,现有服务聚类方法只对单一类型的服务文档进行聚类,缺乏考虑服务的领域特性和服务标签的应用.针对这些问题,本文首先使用本体辅助的支持向量机和面向领域的服务特征降维技术建立服务的特征内容向量,然后使用一种标签辅助的主题服务聚类方法 T-LDA建立融合标签信息之后的隐含主题表示,并利用归一化方法消除通用主题的影响,综合上述方法建立一个面向领域标签辅助的Web服务聚类方法 DTWSC.实验结果表明,该框架能够提高针对不同类型的服务文档的聚类效果.与LDA、K-Means等方法相比,该方法在聚类纯度、熵和F-Measure指标上均具有更好的效果.
The growing number of web services puts forward higher requirements for searching desired web services and clustering Web services can greatly enhance the discovery of Web service. However,the existing clustering approaches are only for a single type of service documents,and they are lacking of considering the domain characteristic and the tags information of services. To solve these problems,the proposed approach constructs the feature vectors of Web service contents by using ontology empowered SVMand domain oriented feature dimension reduction technology. Then a tag aided service clustering model called T-LDA is proposed to construct the hidden topic representations of Web service and general topical information which has less discriminative power is normalized. Finally all methods mentioned above are combined to form the domain oriented and tag aided Web service clustering（ DTWSC）. Experimental results showthat the proposed approach can improve the effect of clustering. Compared with the approaches of LDA and K-means,the proposed approach achieves better performance of the purity,entropy and F-measure.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2015年第7期1266-1274,共9页
Acta Electronica Sinica
基金
国家973重点基础研究发展计划(No.2014CB340404)
国家自然科学基金(No.61373037,No.61202031)
重点实验室开放课题(No.SKLSE 2014-10-07)
