Semantic cluster based Search in UDDI for Health Care Domain

Abstract

Objective: To improve relevancy and personalization of service discovery process of search engines by introducing intelligence in representation of web service called semantic reputation. Methodology: The discovery and distribution of healthcare biological data through web services is being an important characteristic of today's search engine and this process should be efficiently handled. This paper proposes a semantic cluster based recommendation rules approach called semantic reputation for retrieving tagged biological web services. Textual description of biological web services is retrieved by using the semantic web services clustering approach and Ranking is done by recommendation rule based semantic matching. Findings: Retrieving meaningful information is difficult in today's available search engines. Semantic intelligent representation plays a major role in efficiently retrieving meaningful information intelligently. The proposed approach is implemented and compared with the other similar approaches and obtained the recall value of 97.6% and precision value of 98.5% which is better than the existing approaches comparatively by 10% to 19%. Application/Improvement: Our system supports service requestors to retrieve the services relevantly without deviating them to some other fraudulent web sites which proves to be time consuming and trustworthy. Extended work of this paper is to implement these approaches in extending UDDI for efficient web service discovery.