Abstract
Because of the short length of documents describing Web services on the Internet, the traditional modeling method is not ideal, which affects the clustering effect of Web services. To this end, we propose a word-embedded topic model, which can effectively solve the problem of data sparsity. A method based on embedded words and topic models. Firstly, Wikipedia is used as an external corpus to extend API service document, and LF-LDA model is used to model its topic distribution. The corpus data is extracted from Wikipedia by wikiextractor, and the corpus is trained with Word2vec tool. The data comes from its word vector model. Use the trained Wikipedia Word Vector Model to extend API service description documents. In the extended API service documents, LF-LDA topic modeling technology is used to mine the implicit topic information and determine the optimal number of topics, so as to accurately measure the semantic similarity between API service documents. Finally, K-means algorithm is used to cluster Web services, and the validity of this method is verified by the real data of API services on the basis of programmability. The experimental results show that this method has better accuracy, recall rate and F value than the traditional Web service clustering method based on topic model algorithm.
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Acknowledgement
This work is partly supported by the Project Supported by Science and Technology Project in Shaanxi Province of China (Program No.2019ZDLGY07-08) and the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province, China (Grant No.2018KW-049), and the Innovation Program of Xi’an University of Posts and Telecommunications, China (Grant No.CXJJLY2018049).
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Chen, Y., Wang, X., Xia, H., Wang, Z., Yv, Z. (2020). Research on Web Service Clustering Method Based on Word Embedding and Topic Model. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_107
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DOI: https://doi.org/10.1007/978-3-030-32591-6_107
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