Recent Advancements in Semantic Web Service Selection

Abstract

Conventional web services have more minor role of semantics for finding appropriate web services. In semantic web service selection, the maximum matching process plays an important role in finding accurate results using semantics. Most of the pre-existing web service selection approaches work based on keyword-based searching to find the relevant results for the corresponding user query. These approaches neglect the semantic understanding, which leads to irrelevant results for a user query. This paper reviews existing state-of-the-art for semantic web service selection. The techniques discussed here focused on the semantic capabilities and are also applicable for both web service composition and web service selection. Furthermore, two network flow-based approaches for maximum matching are applied, and an improvement in web service selection is observed.

Keywords:

• Semantic web service
• Web service selection
• Semantic similarity
• Semantic matching approaches
• Bipartite matching
• Network flow
• Ontology

ACKNOWLEDGEMENTS

The authors are thankful to Shri. G. S. Institute of Technology and Science, Indore, for providing all necessary support and facilities required to accomplish this research work.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Riddhi Pahariya

Riddhi Pahariya completed her MTech in information technology from Shri G S Institute of Technology & Science, Indore. Presently, she is working in Tata Consultancy Services as assistant system engineer. Her research interests include semantic web service selection. Email: [email protected]

Lalit Purohit

Lalit Purohit earned his PhD degree from the Department of Computer Engineering at Indian Institute of Technology, Roorkee. He is currently working as associate professor at information technology Department of SGSITS Indore, India. His research interests include web services, semantic web service selection, Graph algorithms, evolutionary computing applications, machine learning for web service selection.