Research Article
SC-GAT: Web Services Classification Based on Graph Attention Network
@INPROCEEDINGS{10.1007/978-3-030-67537-0_31, author={Mi Peng and Buqing Cao and Junjie Chen and Jianxun Liu and Bing Li}, title={SC-GAT: Web Services Classification Based on Graph Attention Network}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Web service Attention mechanism Graph attention network Service classification}, doi={10.1007/978-3-030-67537-0_31} }- Mi Peng
Buqing Cao
Junjie Chen
Jianxun Liu
Bing Li
Year: 2021
SC-GAT: Web Services Classification Based on Graph Attention Network
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_31
Abstract
The classification of Web services with high similarity is conducive to the promotion for service management and service discovery. With the increasing number of Web services, how to accurately and efficiently classify the Web services becomes an urgent and challenging task. Although the existing methods achieve significant results in the task for service classification via integrating the structure information of service network with the content features of service node, it fails to discriminate the importance of neighbor services in the service network on the service node needed to be classified. To solve this problem, we propose a Web services classification method based on graph attention network. Firstly, according to the composition and shared annotation relationship of Web services, it applies the description documents, tags of Web services and the call relationship between mashups and services to build a service relationship network. Then, the attention coefficient of service nodes in the network is calculated by the self-attention mechanism, and different service nodes in the neighborhood are assigned different weights to classify Web services. Through the graph attention network, the content features of Web service can be well integrated with its structure information. Also, the learned attention weight is more interpretable. The experimental results on the real dataset of ProgrammableWeb platform show that the precision, recall and macro-F1 of the proposed method are greatly improved compared to those of GCN, Node2vec, DeepWalk and Line.
