微信服务号
微信订阅号
- XIAO Yong
XIAO Yong
Hunan Key Laboratory for Services Computing and Novel Software Technology(Hunan University of Science and Technology), Xiangtan 411201, China;School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Find this author on CNKI
Find this author on BaiDu
Search for this author on this site - LIU Jian-Xun
LIU Jian-Xun
Hunan Key Laboratory for Services Computing and Novel Software Technology(Hunan University of Science and Technology), Xiangtan 411201, China;School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Find this author on CNKI
Find this author on BaiDu
Search for this author on this site - HU Rong
HU Rong
Hunan Key Laboratory for Services Computing and Novel Software Technology(Hunan University of Science and Technology), Xiangtan 411201, China;School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Find this author on CNKI
Find this author on BaiDu
Search for this author on this site - CAO Bu-Qing
CAO Bu-Qing
Hunan Key Laboratory for Services Computing and Novel Software Technology(Hunan University of Science and Technology), Xiangtan 411201, China;School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Find this author on CNKI
Find this author on BaiDu
Search for this author on this site - CAO Ying-Cheng
CAO Ying-Cheng
Hunan Key Laboratory for Services Computing and Novel Software Technology(Hunan University of Science and Technology), Xiangtan 411201, China;School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Find this author on CNKI
Find this author on BaiDu
Search for this author on this site
TP311
National Natural Science Foundation of China (61872139, 61873316, 61702181); Natural Science Foundation of Hunan Province (2018YFB1402800-04, 2018JJ2139, 2018JJ2136, 2018JJ3190)
- Article | |
- Metrics |
- Reference |
- Related |
- Cited by | |
- Comments
With the development of SOA technology, Web service is widely used and the number of services is growing rapidly. It is very important to classify Web service correctly and efficiently to improve the quality of service discovery and promote the efficiency of service composition. However, the existing Web service classification technologies have some problems, such as sparse description text, insufficient consideration of attribute information, and structural relationship. Therefore, it is difficult to effectively improve the accuracy of Web service classification. In order to solve this problem, this study proposes a GAT2VEC-based Web service classification method. Firstly, according to the structural relationship between Web services and their own attribute information, several corresponding structural diagrams and attribute bipartite diagrams are constructed respectively, and the random walk algorithm is used to generate the structural context and attribute context of Web services. Then, the SkipGram model is used to train the joint context to obtain the word vector which merges the multidimensional information. Finally, the SVM model is used to perform the classification and prediction of Web services. The experimental results show that compared with the five methods of Doc2vec, LDA, Deepwalk, Node2Vec, and TriDNR, the proposed method has 135.3%, 60.3%, 12.4%, 10.5%, and 4.3% improvement in Macro F1 value, which effectively improves the accuracy of service classification.
Get Citation
肖勇,刘建勋,胡蓉,曹步清,曹应成.基于GAT2VEC的Web服务分类方法.软件学报,2021,32(12):3751-3767
Copy
Article Metrics
- Abstract:
- PDF:
- HTML:
- Cited by:
History
- Received:November 21,2019
- Revised:March 09,2020
- Online: December 02,2021
- Published: December 06,2021
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
