摘要： 语义Web服务发现机制在发现服务时的准确率较低。为解决该问题，提出一种基于模糊聚类优化的语义Web服务发现方法。采用改进的模糊C-均值(FCM)聚类算法，实现对服务聚类预处理，在模糊聚类时，综合考虑服务的输入、输出、前提、效果 4个功能性参数，并扩展已有的服务匹配机制，在匹配时，将服务的4个功能性参数全部作为服务相似度的计算因子。实验结果 表明，在模糊聚类稳定的条件下，该方法的服务平均查全率为79.6%，平均查准率为85.9%，均高于未采用聚类处理和只采用 输入/输出参数的FCM聚类处理方法。
关键词:
领域本体,
本体描述语言,
本体距离,
模糊聚类,
语义Web服务,
服务发现
Abstract: Aiming at the problem of low efficiency for semantic Web service discovery mechanism in finding service, this paper proposes a novel method based on fuzzy clustering for optimizing semantic Web service discovery. It adopts the modified Fuzzy C-means(FCM) clustering algorithm to realize the cluster preprocessing of services. When clustering services, it can comprehensively consider the input, output, premise and the effect of service as the clustering parameters. This paper expands existing services matching mechanism. When matching services, it can take four functional parameters of service as its factors for similarity calculation. Experimental results show that under in fuzzy clustering stable conditions, the method of service average recall rate of 79.6%, and the average prospective rate of 85.9%, higher than the clustering process and only using Input/Output(I/O) parameters FCM method of clustering processing.
Key words:
domain ontology,
ontology description language,
ontology distance,
fuzzy clustering,
semantic Web service,
service discovery
中图分类号:
王永明, 张英俊, 谢斌红, 潘理虎, 陈立潮. 基于模糊聚类优化的语义Web服务发现[J]. 计算机工程, 2013, 39(7): 219-223.
WANG Yong-Meng, ZHANG Yang-Dun, XIE Bin-Gong, BO Li-Hu, CHEN Li-Chao. Semantic Web Service Discovery Based on Fuzzy Clustering Optimization[J]. Computer Engineering, 2013, 39(7): 219-223.