Web Service Clustering Approach Based on Network and Fused Document-Based and Tag-Based Topics Similarity

Deng Li Ping, Guo Bing and Zheng Wen
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Deng Li Ping: Institute of Public Safety and Big Data, College of Data Science, Taiyuan University of Technology, China
Guo Bing: Department of Computer Science and Technology, Taiyuan Normal University, China
Zheng Wen: Institute of Public Safety and Big Data, College of Data Science, Taiyuan University of Technology, China

International Journal of Web Services Research (IJWSR), 2021, vol. 18, issue 3, 63-81

Abstract: To produce a web services clustering with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach with two models, which are helpful to the service clustering problem. Firstly, a document-tag LDA model (DTag-LDA) is proposed that considers the tag information of web services, and the tag can describe the effective information of documents accurately. Based on the first model, this article further proposes an efficient document weight and tag weight-LDA model (DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively and then merges the two networks to generate web service network clustered. In addition, this article also designs experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. And the proposed method has obvious advantages in precision, recall, purity, and other performance.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:18:y:2021:i:3:p:63-81

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