A Novel Method for Prediction of Difficult Keyword Queries

Authors(2) :-Sonal. G. Chopde, S.A.Murab

Keyword queries are used to access data from databases. To improve the performance of querying system it would be useful to identify queries with low ranking quality. In this paper we analyze the characteristics o hard queries and propose a novel framework to measure the difficulty for a keyword query over a database. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. The ranking quality of the result provides a good user satisfaction. Our intensive experiments show that the algorithms predict the issue of a question with comparatively low errors and negligible time overhead.

Authors and Affiliations

Sonal. G. Chopde
Department of Computer Science and Engineering, Jagdambha College of Engineering and Technology, Yavatmal, Maharashtra, India
S.A.Murab
Department of Computer Science and Engineering, Jagdambha College of Engineering and Technology, Yavatmal, Maharashtra, India

Query Performance, Query Effectiveness, Keyword Query, Robustness, Databases

  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 semistructured 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. G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, "Keyword searching and browsing in databases using BANKS," in Proc. 18th ICDE, San Jose, CA, USA, 2002, pp. 431–440.
  7. C. Manning, P. Raghavan, and H. Schütze, An Introduction to Information Retrieval. New York, NY: Cambridge University Press, 2008.
  8. A. Trotman and Q. Wang, "Overview of the INEX 2010 data centric track," in 9th Int. Workshop INEX 2010, Vugh, The Netherlands, pp. 1–32,
  9. T. Tran, P. Mika, H. Wang, and M.Grobelink,"Semsearch S10," in Proc.3rd Int. WWW Conf., Raleigh, NC, USA, 2010.
  10. S. C. Townsend, Y. Zhou, and B. Croft, "Predicting query performance," in Proc. SIGIR ’02, Tampere, Finland, pp. 299–306.
  11. A. Nandi and H. V. Jagadish, "Assisted querying using instantresponse interfaces," in Proc. SIGMOD 07, Beijing, China, pp. 1156–1158.
  12. 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.
  13. 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.
  14. B. He and I. Ounis, "Query performance prediction," Inf. Syst., vol. 31, no. 7, pp. 585–594, Nov. 2006.
  15. K. Collins-Thompson and P. N. Bennett, "Predicting query performance via classification," in Proc. 32nd ECIR, Milton Keynes, U.K., 2010, pp. 140–152.
  16. A. Shtok, O. Kurland, and D. Carmel,"Predicting query performance by query-drift estimation," in Proc. 2nd ICTIR, Heidelberg, Germany, 2009, pp. 305–312.
  17. 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.
  18. Y. Zhao, F. Scholer, and Y. Tsegay, "Effective pre-retrieval query performance prediction using similarity and variability evidence," in Proc. 30th ECIR, Berlin, Germany, 2008, pp. 52–64.
  19. C. Hauff, L. Azzopardi, and D. Hiemstra, "The combination and evaluation of query performance prediction methods," in Proc.31st ECIR, Toulouse, France, 2009, pp. 301–312.
  20. V.Khalate, S.Gupta," An efficient forecasting of difficult keyword queries over databases,"vol.4, nov.2014.
  21. E. Yom-Tov, S. Fine, D. Carmel, and A. Darlow, "Learning to estimate query difficulty: Including applications to missing content detection and distributed information retrieval," in Proc.
  22. th Annu. Int. ACM SIGIR Conf. Research Development Information Retrieval, Salvador, Brazil, 2005, pp. 512–519.
  23. J. A. Aslam and V. Pavlu, "Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions," in Proc. 29th ECIR, Rome, Italy, 2007, pp. 198–209.

Publication Details

Published in : Volume 2 | Issue 2 | March-April 2016
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 311-316
Manuscript Number : IJSRSET162292
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Sonal. G. Chopde, S.A.Murab, " A Novel Method for Prediction of Difficult Keyword Queries , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.311-316, March-April-2016.
Journal URL : http://ijsrset.com/IJSRSET162292

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