摘要:随着SOA技术的发展，Web服务被广泛应用，服务数量增长迅速.正确高效地对Web服务进行分类，对于提高服务发现质量、促进服务组合效率非常重要.然而，现有的Web服务分类技术存在描述文本稀疏、未充分考虑属性信息以及结构关系等问题，难以有效提升Web服务分类的精度.针对此问题，提出一种基于GAT2VEC的Web服务分类方法.首先，针对Web服务之间的结构关系和自身的属性信息分别构建出多个相对应的结构关系图和属性二分图，并采用随机游走算法生成Web服务的结构上下文和属性上下文；然后，利用SkipGram模型对联合上下文进行训练，得到融合多维信息的表征向量；最后，采用SVM模型实现Web服务的分类预测.在ProgrammableWeb真实数据集上进行对比实验，实验结果表明：相比于Doc2vec，LDA，Deepwalk，Node2vec和TriDNR这5种方法，所提出的方法在Macro F1值上有了135.3%，60.3%，12.4%，10.5%和4.3%的提升，切实提高了服务分类的精度.



Abstract:With the development of SOA technology, Web service is widely used and the number of services is growing rapidly. It is very important to classify Web service correctly and efficiently to improve the quality of service discovery and promote the efficiency of service composition. However, the existing Web service classification technologies have some problems, such as sparse description text, insufficient consideration of attribute information, and structural relationship. Therefore, it is difficult to effectively improve the accuracy of Web service classification. In order to solve this problem, this study proposes a GAT2VEC-based Web service classification method. Firstly, according to the structural relationship between Web services and their own attribute information, several corresponding structural diagrams and attribute bipartite diagrams are constructed respectively, and the random walk algorithm is used to generate the structural context and attribute context of Web services. Then, the SkipGram model is used to train the joint context to obtain the word vector which merges the multidimensional information. Finally, the SVM model is used to perform the classification and prediction of Web services. The experimental results show that compared with the five methods of Doc2vec, LDA, Deepwalk, Node2Vec, and TriDNR, the proposed method has 135.3%, 60.3%, 12.4%, 10.5%, and 4.3% improvement in Macro F1 value, which effectively improves the accuracy of service classification.



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