A GLOBAL FUSION RE-RANKING FOR EFFECTIVE DATA SEARCH WITH CORRELATION MODEL

Author (s) : Mrs.S.GEETHA and Dr.S.BALU

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Abstract :
Surfing on the internet is becoming more prominent in day today life. In searching there are major issues like noisy data and unwanted data. The existing system provides additional results rather than appropriate results and uses PageRank algorithm. PageRank uses link analysis algorithm to measure the page relevance in a hyperlinked set of documents. In order to improve the existing co–diffusion of keywords and ranking, the system introduces the result remerging and re-ranking concepts. Basically ranking will be performed by the popularity, key term and its frequency count.  In the proposed system an enhanced ranking concept is used which improves the performance of the co-diffusion ranking system. The algorithm use here is SEMANTIC DUAL CORRELATION MODEL. It first does the Lexical Analysis and then it performs the basic pre processing which are stemming and stop words. Then the algorithm finds keyword’s co-fusion among different web search engines. The ranking is done locally for two or more search engines and a global re-ranking are done at the end.

Keywords : Effective Data Search, Correlation Model

Conference Date : 17/09/2021

Pages : 45

Paper ID : IESMDT43