Expertization Level Ranking for Query Transformation

Objectives: To develop an approach to find one’s expertization level in a given field. Methods/Statistical Analysis: The search engines were utilized to extract the expert’s data available in the internet. The results generated by the search engines were downloaded in the database. Intern these results were used as an input to the relevance and expertise mapping process. We have used Relevance Algorithm and Expertise Mapping Algorithm to process the data generated by the search engines. These process yields one’s expertization level on a particular area. Findings: E-learning systems were automated to identify an expert by using two methods self-classification and document-based-relevance. These methods assume that relevance of one’s specified keywords or documents to the query is positively related to their expertise. In reality, one can be identified as an expert in a specific domain by the contribution made on that particular domain. The expert expertization data available in websites could be utilized by the e-learning systems to evaluate the expert’s. Hence it is proposed to ensure the expertise level of an expert using a Dynamic Expertization Estimating System (DEES) in an e-learning environment. In this approach, search engines were utilized as an agent to extract the expert’s data available in the internet and weightage were given to the expert according to their contribution made by the expert towards the given expertise area. The results retrieved by applying this mechanism yielded data with high accuracy levels to ensure the expertization level of an expert. Application/Improvements: Connecting the Dynamic Expertization Estimating System (DEES) with social media to fetch more data pertaining to the expert expertization and produce accurate expertise level for each expert.