A proposed framework to optimize the query by filtering noise using Semantic information processing

The existing search engines retrieve information only based on the keywords. The incapability to search on the basis of the relation between the keywords and the user concepts, generates noise and hence, results in irrelevant retrieval. This leads to the idea of performing Semantic information processing by mapping the user´s Concept and Context of the query with the retrieved results to filter (remove) noise from the query. The present study suggests Noise Removal for Semantic Information Processing (NRSIP) framework for the Search Problem (SP): find expert(s) in Panjab University. The framework allows the user to perform categorical search. Instead of inputting all the keywords, it allows the user to select the best matched option from the available choices to formulate the Semantic Query (SQ). The relevant information for the SQ is then retrieved by the Present Search Engine (PSE) used. The performance analysis shows that the proposed NRSIP framework improves the retrieval time and effectiveness for retrieving relevant web documents as compared to PSE.