Paper Info
Title
Web Services Classification Based on Wide & Bi-LSTM Model.
Abstract
With the rapid growth of Web services on the Internet, it becomes a great challenge for Web services discovery. Classifying Web services with similar functions is an effective method for service discovery and management. However, the functional description documents of Web services usually are short in their length, with sparse features and less information, which makes most topic models unable to model the short text well, consequently affecting the Web service classification. To solve this problem, a Web service classification method based on Wide & Bi-LSTM model is proposed in this paper. In this method, first, all the discrete features in the description documents of Web services are combined to perform the breadth prediction of Web service category by exploiting the wide learning model. Second, the word order and context information of the words in the description documents of Web services are mined by using the Bi-LSTM model to perform the depth prediction of the Web service category. Third, it uses the linear regression algorithm to integrate the breadth and depth prediction results of Web service categories as the final result of the service classification. Finally, compared with sixWeb service classification methods based on TF-IDF, LDA, WE-LDA, LSTM, Wide&Deep, and Bi-LSTM, respectively, the experimental results show that our approach achieves a better performance in the accuracy of Web service classification.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2907546
IEEE ACCESS
Keywords
Field
DocType
Wide learning model,Bi-LSTM model,linear regression,web service classification
World Wide Web,Computer science,Computer network,Web service
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.36
0
6
Name
Order
Citations
PageRank
Hongfan Ye110.70
Buqing Cao220023.96
Zhenlian Peng361.87
Ting Chen42154268.96
Yiping Wen5247.31
Jianxun Liu6585.07