An automatic approach for tagging Web services using machine learning techniques1
| Autor: | David W. Cheung, Maria Lin |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Web analytics
Web standards medicine.medical_specialty Web 2.0 Web development Computer Networks and Communications Computer science 02 engineering and technology computer.software_genre World Wide Web Search engine Artificial Intelligence 020204 information systems Web design 0202 electrical engineering electronic engineering information engineering medicine Web navigation Data Web WS-Addressing Information retrieval business.industry Web application security 020201 artificial intelligence & image processing Web mapping Web service WS-Policy business Web intelligence computer Web modeling Software |
| Zdroj: | Web Intelligence. 14:99-118 |
| ISSN: | 2405-6464 2405-6456 |
| DOI: | 10.3233/web-160334 |
| Popis: | Web services have become popular and increasingly important in e-business and e-commerce applications especially in large scale distributed systems. As a result, an increasing number of Web services have been developed. However, this huge collection of Web services makes the task of locating a suitable one more challenging as well as more difficult. Automatic clustering of Web services groups services with similar functions together. Clustering could greatly boost the power of Web service search engines and generate tags to improve the search accuracy of tag-based service recommendation. In this paper, we propose a Web service clustering technique based on Carrot search clustering and K-means to group similar services together. These clustered groups are then tagged. We also develop a tag-based service recommendation for WSDL documents using naive bayes algorithm to classify Web services into different tags. We demonstrate that the proposed clustering approach is effective for Web service discovery through two sets of real data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|