Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

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

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  • @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
Xiangping Zhang1,*, Jianxun Liu1,*, Buqing Cao1,*, Qiaoxiang Xiao1,*, Yiping Wen1,*
  • 1: Hunan University of Science and Technology
*Contact email: zxpkpnm@gmail.com, ljx529@gmail.com, buqingcao@gmail.com, 18390219693@163.com, ypwen81@gmail.com

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.