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Web Services Classification with Topical Attention Based Bi-LSTM

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2019)

Abstract

With the rapid growth of the number of Web services on the Internet, how to classify web services correctly and efficiently become more and more important in the development and application of Web services. Existing function-based service clustering techniques have some problems, such as the sparse document semantics, unconsidered word order and the context information, so the accuracy of service classification needs to be further improved. To address this problem, this paper exploits the attention mechanism to combine the local implicit state vector of Bi-LSTM and the global LDA topic vector, and proposes a method of Web services classification with topical attention based Bi-LSTM. Specifically, it uses Bi-LSTM to automatically learn the feature representation of Web service. Then, it utilizes the offline training to obtain the topic vector of Web service document and performs the topic attention strengthening processing for Web service feature representation, and obtains the importance or weight of the different words in Web service document. Finally, the enhanced Web service feature representation is used as the input of the softmax neural network layer to perform the classification prediction of Web service. The experimental results validate the efficiency and effectiveness of the proposed method.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant 61572187, Grant 61872139, and Grant 61702181, in part by the Natural Science Foundation of Hunan Province under Grant 2017JJ2098, Grant 2018JJ2136, and Grant 2018JJ2139, and in part by the Educational Commission of Hunan Province of China under Grant 17C0642.

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Authors and Affiliations

  1. Hunan University of Science and Technology, Xiangtan, China

    Yingcheng Cao, Jianxun Liu, Buqing Cao, Min Shi, Yiping Wen & Zhenlian Peng

  2. Key Laboratory of Knowledge Processing and Networked Manufacturing, HNUST, Xiangtan, China

    Yingcheng Cao, Jianxun Liu, Buqing Cao, Min Shi, Yiping Wen & Zhenlian Peng

Authors
  1. Yingcheng Cao
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  2. Jianxun Liu
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  3. Buqing Cao
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  4. Min Shi
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  5. Yiping Wen
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  6. Zhenlian Peng
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Corresponding author

Correspondence to Jianxun Liu .

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Editors and Affiliations

  1. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  2. Shanghai University, Shanghai, China

    Honghao Gao

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. University of Exeter, Exeter, UK

    Geyong Min

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

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Cao, Y., Liu, J., Cao, B., Shi, M., Wen, Y., Peng, Z. (2019). Web Services Classification with Topical Attention Based Bi-LSTM. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-30146-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30145-3

  • Online ISBN: 978-3-030-30146-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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