Research Article
Web Service Discovery Based on Information Gain Theory and BiLSTM with Attention Mechanism
@INPROCEEDINGS{10.1007/978-3-030-12981-1_45, author={Xiangping Zhang and Jianxun Liu and Buqing Cao and Qiaoxiang Xiao and Yiping Wen}, title={Web Service Discovery Based on Information Gain Theory and BiLSTM with Attention Mechanism}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Web service clustering Mashup creation Information gain Attention layer BiLSTM}, doi={10.1007/978-3-030-12981-1_45} }
- Xiangping Zhang
Jianxun Liu
Buqing Cao
Qiaoxiang Xiao
Yiping Wen
Year: 2019
Web Service Discovery Based on Information Gain Theory and BiLSTM with Attention Mechanism
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_45
Abstract
Web service discovery is an important problem in service-oriented computing with the increasing number of Web services. Clustering or classifying Web services according to their functionalities has been proved to be an effective way to Web service discovery. Recently, semantic-based Web services clustering exploits topic model to extract latent topic features of Web services description document to improve the accuracy of service clustering and discovery. However, most of them don’t consider deep and fine-grained level information of description document, such as the weight (importance) for each word or the word order. While the deep and fine-grained level information can be fully used to argument service clustering and discovery. To address this problem, we proposed a Web service discovery approach based on information gain theory and BiLSTM with attention mechanism. This method firstly obtains the effective words through information gain theory and then adds them to an attention-based BiLSTM neural network for Web service clustering. The comparative experiments are performed on ProgrammableWeb dataset, and the results show that a significant improvement is achieved for our proposed method, compared with baseline methods.