A Multilevel Scoring Mechanism to Compute Top - K Routing Plans for a Keyword Query
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Abstract
In recent years Keyword search over database is explored. For information retrieval keyword query used, but due to ambiguity of multiple queries over database should be explored. while getting multiple result to keyword query we need effective crawlers, if search engine might be give multiple result to the single query then computation of all the these results and suggesting best one among all result defined as problem statement. In this paper, the label ranking system over unpredictable is presented. The Keyword directing strategy is utilized to course the catchphrases to significant source. In this methodology two techniques are incorporated. If user gives a keyword query to the search engine then the search engine should process the query and returns the appropriate result based rank. The result construction done based on R-Tree and it allows NN queries should be computed and based on I-Index we will construct the score for each NN query result.
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References
Wangchao Le, Feifei Li, Anastasios Kementsietsidis, Songyun Duan, Scalable Keyword Search on Large RDF Data", IEEE2013.
George Kollios, Michalis Potamias, and EvimariaTerzi, Clustering Large Probabilistic Graphs, IEEE vol. 25, NO. 2, February 2013
Ye Yuan, Guoren Wang, Lei Chen, and HaixunWang, Efficient Keyword Search on Uncertain Graph Data, IEEE vol. 25, no. 12, December 2013.
Jun Gao, Jiashuai Zhou, Jeffrey Xu Yu, and TengjiaoWang, Shortest Path Computing in Relational DBMSs, IEEE vol. 26, no. 4, April 2014.
ZhaonianZou, Jianzhong Li, Member, IEEE, Hong Gao, and Shuo Zhang, Mining Frequent Subgraph Patterns from UncertainGraphData,IEEEvol.22,no.9,September 2010.
Lifang Qiao, Yu Wang, A Keyword Query Method for Uncertain Database, 2nd International Conference on Computer Science and Network Technology, IEEE, 2012.
Bolin Ding, Jeffrey Xu Yu, Shan Wang, Lu Qin, Xiao Zhang, Xuemin Lin, Finding Top-k Min-Cost Connected Trees in Databases,IEEE1- 4244-0803-2/07/2007.
Thanh Tran and Lei Zhang, Keyword Query Routing, IEEE vol. 26, no. 2, February2014.
Ye Yuan, Guoren Wang, Haixun Wang, Lei Chen, Efficient Subgraph Search over Large Uncertain Graphs. In Proceedings of the VLDB Endowment, Vol. 4,pp. 876-886, 2011.
HaoHe,HaixunWang,JunYang,PhilipS.Yu,BLINKS: Ranked Keyword Searches on Graphs, SIGMOD'07, June 2007.
Haoliang Jiang, HaixunWang, Philip S. Yu, and Shuigeng Zhou GString: A novel approach for efficient search in graph databases. In ICDE, 2007.
DennisShasha, Jason T.L.Wang, and RosalbaGiugno. Algorithmics and applications of tree and graph searching. In PODS, pages 39–52, 2002.
Xifeng Yan, Philip S. Yu, and Jiawei Han. Substructure similarity search in graph databases. In SIGMOD, pages 766– 777, 2005.
Branimir T. Todorovic, Svetozar R. Rancic, Ivica M. Markovic, Eden H. Mulalic, Velimir M. Ilic, ―Named Entity Recognition and Classification using Context Hidden Markov Model, 9th Symposium on Neural Network Application in Electrical Engineering, NEUREL, pp. 43-46, 2008.
DekaiWu,Weifeng Su andMarineCarpuat,―AKernel PCA Method for Superior Word Sense Disambiguation, Proceedings of the 42nd Meeting of the Association for Computational Linguistics, pp. 637-644, 2004.
AbdelazizZitouni, AsmaDamankesh, ForooghBarakati, Maha Atari,MohamedWatfa, FarhadOroumchian,―Corpusbased Arabic Stemming Using N-grams, Asia Information Retrieval Symposium - AIRS, vol. 6458, pp. 280-289, 2010.
Hassan Mohamed, Nazlia Omar, MohdJuzaidinAb Aziz, ―StatisticalMalayPart-of-Speech (POS) Tagger using Hidden Markov Approach, International Conference on Semantic Technology and Information Retrieval, pp. 231-236, June 2011.