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.
[1]
Xiaotao Huang,et al.
A Relation-Based Search Engine in Semantic Web
,
2007,
IEEE Transactions on Knowledge and Data Engineering.
[2]
M. M. Sufyan Beg.
A subjective measure of web search quality
,
2005,
Inf. Sci..
[3]
Neha Gulati,et al.
Analysis and Comparison of Existing Cognitive Agents used in Web Searching
,
2015
.
[4]
C. Lee Giles,et al.
A Strategic Perspective on Search Engines: Thought Candies for Practitioners and Researchers
,
2009
.
[5]
S. Sasi,et al.
Domain and range identifier module for semantic web search engines
,
2012,
2012 International Conference on Data Science & Engineering (ICDSE).
[6]
Hyo-Won Suh,et al.
A personalized query expansion approach for engineering document retrieval
,
2014,
Adv. Eng. Informatics.