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中文摘要：
目前,传统的基于语法的Web服务发现机制智能性较差,已无法满足用户需求。而基于语义的Web服务发现针对不同的领域和情景都需建立本体库,为发现机制的建立带来了一定的复杂性。将以上两者进行结合,提出一种基于WordNet和Kernel方法的Web服务发现机制。首先利用Kernel-WordNet-VSM对服务进行分类,其中,WordNet用作对抽取的特征向量降维,并采用Kernel函数计算向量之间的相似度。然后利用WordNet概念链中词之间的最短路径,从服务的功能属性方面,对用户的原始请求和服务进行语义层次上的匹配。从而在服务分类的精度上得到一定提高,在服务发现的智能性方面得到了一定的改善。
英文摘要：
At present,because of the poor intelligence,the traditional syntactic-based service discovery has been unable to satisfy customer＇s expectation. For building ontology library in the different areas and situations,establishment of mechanism for the semantic-based Web service discovery is very complicated.Combining the above two approachs,a mechanism for Web service discovery based on WordNet and the Kernel function is proposed in this paper.First,choose Kernel-WordNet-VSM can be utilized to solve the services classification,and WordNet is used to make vector dimension reduction on the extracted feature,Kernel function also calculate the similarity between vectors.Then minimum path between the words in the concept chain of wordNet is used to match user＇s requests and service on the functional attributes of service based on semantic level.Thus in terms of intelligence service discovery has been improved to some extend while the accuracy is improved in classification of services on a certain.
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