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Web Service Discovery Based on Information Gain Theory and BiLSTM with Attention Mechanism

  • Xiangping Zhang
  • Jianxun Liu
  • Buqing CaoEmail author
  • Qiaoxiang Xiao
  • Yiping Wen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

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.

Keywords

Web service clustering Mashup creation Information gain Attention layer BiLSTM 

Notes

Acknowledgements

The work was supported by the Hunan Provincial Natural Science Foundation of China under grant No. 2017JJ2098, 2017JJ4036, 2018JJ2139, 2018JJ2136, Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under grant No. SKLNST-2016-2-26, National Natural Science Foundation of China under grant No. 61572187, 61772193, 61702181, 61872139, 61873316, Innovation Platform Open Foundation of Hunan Provincial Education Department of China under grant No. 17K033.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Xiangping Zhang
    • 1
  • Jianxun Liu
    • 1
  • Buqing Cao
    • 1
    Email author
  • Qiaoxiang Xiao
    • 1
  • Yiping Wen
    • 1
  1. 1.Key Laboratory of Knowledge Processing and Networked ManufacturingHunan University of Science and TechnologyXiangtanChina

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