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Journal of Applied Sciences
  Year: 2013 | Volume: 13 | Issue: 3 | Page No.: 485-490
DOI: 10.3923/jas.2013.485.490
 
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Intelligent Query Refine and Expansion Model for the Retrieval of E-learning Resources

T. Chellatamilan and R.M. Suresh

Abstract:
Today Internet Search Engines, employing intelligent and innovative programming skills, do almost exhaustive search of data contained in websites through number crunching operations at enormous speed but as the search results usually run into hundreds of pages, it is not therefore always possible for an average user to make effective use of such huge collections of data produced as output. While input queries to the Search Engines are usually in the form bag of words, most Information Retrieval systems of Search Engines use models based on statistics of word counts for identifying and ranking the documents in the order of their relevance. In order to make further progress and achieve refinements, researchers have extensively studied the application of Artificial Intelligence (AI), concept of Intelligent Agent (IA) to Information Retrieval Tasks. In this study, the query reformulation has been performed based on the statistical probabilistic language modeling technique. The comparative study on the precision recall before and after query expansion been studied and the plot has been plotted.
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How to cite this article:

T. Chellatamilan and R.M. Suresh, 2013. Intelligent Query Refine and Expansion Model for the Retrieval of E-learning Resources. Journal of Applied Sciences, 13: 485-490.

DOI: 10.3923/jas.2013.485.490

URL: https://scialert.net/abstract/?doi=jas.2013.485.490

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