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.
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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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Zhang, X., Liu, J., Cao, B., Xiao, Q., Wen, Y. (2019). Web Service Discovery Based on Information Gain Theory and BiLSTM with Attention Mechanism. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_45
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