An Efficient Difficult Keyword Prediction using Clustered Based Model View Similarity Matrix
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International Journal of Computer & Organization Trends (IJCOT) |  |
| © 2017 by IJCOT Journal | ||
| Volume - 8 Issue - 1 |
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| Year of Publication : 2018 | ||
| Authors : Palakurthi Sahitya, E. Deepthi |
Citation
Palakurthi Sahitya, E. Deepthi "An Efficient Difficult Keyword Prediction using Clustered Based Model View Similarity Matrix", International Journal of Computer & organization Trends (IJCOT), V8(1):19-23 January - February 2018, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract
To the best of our knowledge, there has not been any work on predicting or analysing the difficulties of queries over databases. Researchers have proposed some methods to detect difficult queries over plain text document collections. However, these techniques are not applicable to our problem since they ignore the structure of the database. In particular, as mentioned earlier, a Keyword query interface must assign each query term to a schema element in the database. It must also distinguish the desired result type. We empirically show that direct adaptations of these techniques are ineffective for structured data. In this paper we are propose topic based cluster search algorithm for search of keyword in the database. By implementing this technique we can improve more efficiency of query oriented keyword search.
Reference
[1] V. Hristidis, L. Gravano, and Y. Papakonstantinou, “Efficient IRstyle keyword search over relational databases,” in Proc. 29th VLDB Conf., Berlin, Germany, 2003, pp. 850–861.
[2] Y. Luo, X. Lin, W. Wang, and X. Zhou, “SPARK: Top-k keyword query in relational databases,” in Proc. 2007 ACM SIGMOD, Beijing, China, pp. 115–126.
[3] V. Ganti, Y. He, and D. Xin, “Keyword++: A framework to improve keyword search over entity databases,” in Proc. VLDB Endowment, Singapore, Sept. 2010, vol. 3, no. 1–2, pp. 711–722.
[4] J. Kim, X. Xue, and B. Croft, “A probabilistic retrieval model for semi structured data,” in Proc. ECIR, Tolouse, France, 2009, pp. 228–239.
[5] N. Sarkas, S. Paparizos, and P. Tsaparas, “Structured annotations of web queries,” in Proc. 2010 ACM SIGMOD Int. Conf. Manage. Data, Indianapolis, IN, USA, pp. 771–782.
[6]J. A. Aslam and V. Pavlu, “Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions,” in Proc. 29th ECIR, Rome, Italy, 2007,. 198–209.
[7] O. Kurland, A. Shtok, S. Hummel, F. Raiber, D. Carmel, and O. Rom, “Back to the roots: A probabilistic framework for query performance prediction,” in Proc. 21st Int. CIKM, Maui, HI, USA, 2012, pp. 823–832.
[8] O. Kurland, A. Shtok, D. Carmel, and S. Hummel, “A Unified framework for post-retrieval query-performance prediction,” in Proc. 3rd Int. ICTIR, Bertinoro, Italy, 2011, pp. 15–26.
[9] S. Cheng, A. Termehchy, and V. Hristidis, “Predicting the effectiveness of keyword queries on databases,” in Proc. 21st ACM Int. CIKM, Maui, HI, 2012, pp. 1213-1222..
[10] E. Demidova, P. Fankhauser, X. Zhou, and W. Nejdl, “DivQ: Diversification for keyword search over structured databases,” in Proc. SIGIR’ 10, Geneva, Switzerland, pp. 331–338.
[11] Y. Zhou and B. Croft, “Ranking robustness: A novel framework to predict query performance,” in Proc. 15th ACM Int. CIKM, Geneva, Switzerland, 2006, pp. 567–574.
[12] B. He and I. Ounis, “Query performance prediction,” Inf. Syst., vol. 31, no. 7, pp. 585–594, Nov. 2006.
[13] K. Collins-Thompson and P. N. Bennett, “Predicting query performance via classification,” in Proc. 32nd ECIR, Milton Keynes, U.K., 2010, pp. 140–152.
[14] A. Shtok, O. Kurland, and D. Carmel, “Predicting query performance by query-drift estimation,” in Proc. 2nd ICTIR, Heidelberg, Germany, 2009, pp. 305–312.
[15] Y. Zhou and W. B. Croft, “Query performance prediction in web search environments,” in Proc. 30th Annu. Int. ACM SIGIR, New York, NY, USA, 2007, pp. 543–550.
Keywords
Keyword, Clustering Data Mining, Query Searching, Difficult Keyword.