We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Semantic based Personalized Framework for Information Retrieval

by K. Saravanakumar, Mahesh Moturi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 20 - Number 4
Year of Publication: 2011
Authors: K. Saravanakumar, Mahesh Moturi
10.5120/2423-3254

K. Saravanakumar, Mahesh Moturi . Semantic based Personalized Framework for Information Retrieval. International Journal of Computer Applications. 20, 4 ( April 2011), 14-17. DOI=10.5120/2423-3254

@article{ 10.5120/2423-3254,
author = { K. Saravanakumar, Mahesh Moturi },
title = { Semantic based Personalized Framework for Information Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 20 },
number = { 4 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume20/number4/2423-3254/ },
doi = { 10.5120/2423-3254 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:53.346576+05:30
%A K. Saravanakumar
%A Mahesh Moturi
%T Semantic based Personalized Framework for Information Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 20
%N 4
%P 14-17
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traditional information retrieval systems are mostly keyword-based and retrieve documents or information by matching keywords. These systems lack a meaningful description for information, so it is difficult for users to find more relevant information. To provide what a user really needs, a framework of information retrieval based on semantics has been proposed in this paper. In this framework the semantics in the user query are identified and these are summarized according to the context. Then the results are classified into possible domains or groups and displayed to user according to his choice from domain the results are re-ranked. By this framework we provide users a convenient and more precise search service with personalization.

References
  1. Ming-Yen Chen, Hui-Chuan Chu ,Yuh-Min Chen “ Developing a Semantic–enable Information Retrieval mechanism ” Expert system with Applications 37(2010) 322-240
  2. P. Nithya, V. Vidya, Dr. L. Ganesan “Development of Semantic Based Information Retrieval using Word-Net Approach” Second International Conference on Computer and Network Technology.
  3. Hochul Jeon, Taehwan Kim, Joongmin Choi “Adaptive User Profiling for Personalized Information Retrieval” Third 2008 International Conference on Convergence and Hybrid Information Technology.
  4. Taehwan Kim, Hochul Jeon, Joongmin Choi“Personalized Information Retrieval using user history” 2008 International Conference on Multimedia and Ubiquitous Engineering
  5. Wang Hongsheng, Shu Xiaoming “Personalized Information Filtering Based on Semantic Similarity”
  6. Ricardo Baeza-Yates, Berthier Ribeiro-Neto “Modern Information Retrieval”
  7. Abdelali. A, Cowie. J, & Soliman. H. S, “Improving query precision using semantic expansion” 2007 Information processing and Management , 43, 705-716.
  8. Oh. H. J. Myaeng, & Jang. M. G “Semantic passage segmentation based on sentence topics for question answering “ 2007 Information Sciences, 177, 3696-3717.
  9. B. Fung, K. Wang and M. Ester. “Hierarchical Document Clustering Using Frequent Itemsets”.SDM’ 03, pp. 59-70.
Index Terms

Computer Science
Information Sciences

Keywords

Semantic Identification Personalization Query processor