DocumentCode
2359374
Title
Information Retrieval using probability and belief theory
Author
Chowdhary, K.R. ; Bansal, V.S.
Author_Institution
M.B.M. Eng. Coll., Jodhpur, India
fYear
2011
fDate
22-24 April 2011
Firstpage
188
Lastpage
191
Abstract
This paper presents two approaches for Information Retrieval (IR) from a collection of documents: Bayesian theory of probability and Dempster-Shafer theory of belief functions. Each method has been supported with essential derivations to prove their suitability for IR. The conclusions of derivations have been applied in illustrative examples. Finally, comparison of both the methods suggests the suitability of each for specific domains.
Keywords
belief networks; inference mechanisms; information retrieval; probability; Bayesian theory; Dempster-Shafer theory; belief function; belief theory; document retrieval; information retrieval; probability; Animals; Bayesian methods; Computational modeling; Indexes; Information retrieval; Probabilistic logic; Uncertainty; Bayesian; Dempster-Shafer; Document retrieval; Information Retrieval;
fLanguage
English
Publisher
ieee
Conference_Titel
Emerging Trends in Networks and Computer Communications (ETNCC), 2011 International Conference on
Conference_Location
Udaipur
Print_ISBN
978-1-4577-0239-6
Type
conf
DOI
10.1109/ETNCC.2011.5958513
Filename
5958513
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