Title: Web services classification via combining Doc2Vec and LINE model

Authors: Hongfan Ye; Buqing Cao; Jinkun Geng; Yiping Wen

Addresses: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

Abstract: Classifying web services with similar functionality from tremendous amount of web services can significantly improve the efficiency of service discovery. Few of the web services classification researches integrate the independent mining of the content semantic information and network structure information hidden in the web service characterisation documents. Therefore, we propose a web service classification method combining them. So, the Doc2Vec algorithm is firstly exploited to deeply mine the functional semantics of web service characterisation documents and obtain web service's content semantic representation. Then, the LINE algorithm is adopted to embed the web service information network which is constructed by utilising web service characterisation documents and word information. Subsequently, the content semantic representation and network structure representation of web service are integrated as the input of the logistic regression classifier to perform web service classification. The experimental results on the ProgrammableWeb dataset verify that the proposed method outperforms to baseline methods.

Keywords: web services classification; content semantic; network structure; LINE; Doc2Vec.

DOI: 10.1504/IJCSE.2020.111433

International Journal of Computational Science and Engineering, 2020 Vol.23 No.3, pp.250 - 261

Received: 24 Apr 2020
Accepted: 16 May 2020

Published online: 24 Nov 2020 *

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