Name
Abstract | ||
|---|---|---|
Clustering Web services that groups together services with similar functionalities helps improve both the accuracy and efficiency of the Web service search engines. An important limitation of existing Web service clustering approaches is that they solely focus on utilizing WSDL (Web Service Description Language) documents. There has been a recent trend of using user-contributed tagging data to improve the performance of service clustering. Nonetheless, these approaches fail to completely leverage the information carried by the tagging data and hence only trivially improve the clustering performance. In this paper, we propose a novel approach that seamlessly integrates tagging data and WSDL documents through augmented Latent Dirichlet Allocation (LDA). We also develop three strategies to preprocess tagging data before being integrated into the LDA framework for clustering. Comprehensive experiments based on real data and the implementation of a Web service search engine demonstrate the effectiveness of the proposed LDA-based service clustering approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-45005-1_12 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Data mining,Latent Dirichlet allocation,Search engine,Information retrieval,Computer science,Web service clustering,Web service,Cluster analysis,Service discovery | Conference | 8274 |
Issue | ISSN | Citations |
null | 0302-9743 | 46 |
PageRank | References | Authors |
1.42 | 22 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Chen | 1 | 313 | 36.77 |
| Yilun Wang | 2 | 297 | 13.03 |
| Qi Yu | 3 | 770 | 55.65 |
| Zibin Zheng | 4 | 3731 | 199.37 |
| Jian Wu | 5 | 933 | 95.62 |