Name
Title | ||
|---|---|---|
Discovering web services in social web service repositories using deep variational autoencoders |
Abstract | ||
|---|---|---|
•We explore the use of Variational Autoencoders for syntactic Web Service discovery.•We evaluate our approach using a 17113-service dataset, the largest among the research community.•Our approach outperforms service engines based on traditional dimensionality reduction techniques (LSA, LDA).•Our approach outperforms service engines based on Word Embeddings.•Average query processing times and VAE training times confirm that our approach is viable in practice. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ipm.2020.102231 | Information Processing & Management |
Keywords | DocType | Volume |
Service-oriented computing,Web Services,Service discovery,Deep neural network,Variational autoencoder | Journal | 57 |
Issue | ISSN | Citations |
4 | 0306-4573 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignacio Lizarralde | 1 | 2 | 1.37 |
| Cristian Mateos | 2 | 430 | 43.09 |
| Alejandro Zunino | 3 | 638 | 53.15 |
| Tim A. Majchrzak | 4 | 231 | 30.59 |
| Tor-Morten Grønli | 5 | 81 | 23.80 |