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10.1155/2015/931258 http://scihub22266oqcxt.onion/10.1155/2015/931258 C4736216!4736216!26933673     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26933673.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       ScientificWorldJournal 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=26933673|t=2015. Semantic Clustering of Search Engine Results |pdf=|usr=}}{{26933673}} gab.com Text Semantic Clustering of Search Engine Results JO: ScientificWorldJournal http://www.kidney.de/pget.php?&tw=1&pmid=26933673 #FOAvip This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision. Twit Text FOAVip Semantic Clustering of Search Engine Results ????ScientificWorldJournal http://www.kidney.de/pget.php?&tw=1&pmid=26933673 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26933673 #FOAvip English Wikipedia <ref>{{Cite pmid|26933673}}</ref> Semantic Clustering of Search Engine Results #MMPMID26933673 Soliman SS; El-Sayed MF; Hassan YF ScientificWorldJournal 2015[]; 2015 (ä): ä PMID26933673show ga This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision. äSemantic Clustering of Search Engine Results (epmc).pdf DeepDyve Pubget Overpricing