Name
Abstract | ||
|---|---|---|
Correlative approaches have attempted to cluster web services based on either the explicit information contained in service descriptions or functionality semantic features extracted by probabilistic topic models. However, the implicit contextual information of service descriptions is ignored and has yet to be properly explored and leveraged. To this end, we propose a novel framework with deep neural network, called DeepWSC, which combines the advantages of recurrent neural network and convolutional neural network to cluster web services through automatic feature extraction. The experimental results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICWS.2019.00077 | 2019 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
Web service,service clustering,deep learning,probabilistic topic model,word embedding | Data mining,Convolutional neural network,Computer science,Recurrent neural network,Artificial intelligence,Word embedding,Probabilistic logic,Deep learning,Topic model,Artificial neural network,Web service | Conference |
ISBN | Citations | PageRank |
978-1-7281-2718-7 | 1 | 0.35 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guobing Zou | 1 | 95 | 20.12 |
| Zhen Qin | 2 | 4 | 1.07 |
| Qiang He | 3 | 217 | 23.35 |
| Pengwei Wang | 4 | 9 | 4.04 |
| Bofeng Zhang | 5 | 10 | 3.86 |
| Yanglan Gan | 6 | 13 | 3.96 |