Name
Title | ||
|---|---|---|
Web Service Classification Based On Information Gain Theory And Bidirectional Long Short-Term Memory With Attention Mechanism |
Abstract | ||
|---|---|---|
With the increasing number of Web services, Web service discovery for service-oriented application development has become more important. Clustering or classifying Web services according to their functionalities is an effective way for Web service discovery. Extracting latent topic features from service description by exploiting topic model can improve the accuracy of service classification. However, most of them simply treat the description document as a set of flat word features without considering the varying importance of different features as well as sequential relations between features. In this article, we proposed a Web service classification approach based on information gain theory and bidirectional long short-term memory with attention mechanism for accuracy Web service classification by considering fine-grained factors implicit in Web service description. The comparative experiments are performed on ProgrammableWeb dataset, and show that the proposed method achieves a significant improvement compared with baseline methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/cpe.6202 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
attention mechanism, BiLSTM, information gain, Web service classification | Journal | 33 |
Issue | ISSN | Citations |
13 | 1532-0626 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangping Zhang | 1 | 3 | 3.12 |
| Jianxun Liu | 2 | 640 | 67.12 |
| Buqing Cao | 3 | 9 | 5.93 |
| Min Shi | 4 | 11 | 4.31 |