Skip to main content
SpringerLink
Log in

Web Query Reformulation Using Differential Evolution

  • Conference paper
Trends in Applied Intelligent Systems (IEA/AIE 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6097))

  • 1179 Accesses

Abstract

This paper presents a query reformulation and clustering technique using Differential Evolution. Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. The proposed DE automatically determines the type of a query and new pattern of query reformulation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baeza-Yates, R.: Graphs from search engine queries. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 1–8. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  2. Boldi, P., Bonchi, F., Castillo, C., Donato, D., Vigna, S.: Query suggestions using query-flow graphs. In: Proc. Of workshop on Web Search Click Data, WSCD 2009 (2009)

    Google Scholar 

  3. Radlinski, F., Joachims, T.: Query chains: learning to rank from implicit feedback. In: KDD 2005: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 239–248. ACM Press, New York (2005)

    Chapter  Google Scholar 

  4. Lau, T., Horvitz, E.: Patterns of search: analyzing and modeling web query refinement. In: Proc. of Conf. on User modeling, UM 1999 (1999)

    Google Scholar 

  5. Rieh, S.Y., Xie, H.: Analysis of multiple query reformulations on the web: the interactive information retrieval context. Inf. Process. Management. 42(3), 751–768 (2006)

    Article  Google Scholar 

  6. Glance, N.S.: Community search assistant. In: Artificial Intelligence for Web Search, pp. 91–96 (2001)

    Google Scholar 

  7. Craswell, N., Szummer, M.: Random walks on the click graph. In: Proc. of ACM SIGIR SIGIR 2007 (2007)

    Google Scholar 

  8. Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., Vigna, S.: The query-flow graph: Model and applications. In: Proc. of ACM conf. on Inf. and Knowledge Manage. CIKM 2008 (2008)

    Google Scholar 

  9. Konar, A.: Computational Intelligence: Principles, Techniques and Applications. Springer, Berlin (2005)

    Book  MATH  Google Scholar 

  10. Chou, H., Su, M.C., Lai, E.: A new cluster validity measure and its application to image compression. Pattern Anal. Appl. 7(2), 205–220 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSAS, University of New Brunswick, New Brunswick, Canada

    Prabhat K. Mahanti & Feras Al-Obeidat

  2. King Abdul Aziz University, Faculty of Computing & Information Technology, Saudi Arabia

    Mohammad Al-Fayoumi

  3. Department of Information Technology, Birla Institute of Technology Mesra, India

    Soumya Banerjee

Authors
  1. Prabhat K. Mahanti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Al-Fayoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Soumya Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feras Al-Obeidat
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computing and Numerical Analysis, University of Cordoba, Campus Universitario de Rabanales, Einstein Building, 3rd floor,, 14071, Cordoba, Spain

    Nicolás García-Pedrajas

  2. Dept. of Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain

    Francisco Herrera

  3. School of Computing, University of the West of Scotland, PA1 2BE, Paisley, UK

    Colin Fyfe

  4. Dept. Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain

    José Manuel Benítez

  5. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, TX 78666-4616, San Marcos, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mahanti, P.K., Al-Fayoumi, M., Banerjee, S., Al-Obeidat, F. (2010). Web Query Reformulation Using Differential Evolution. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13025-0_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13024-3

  • Online ISBN: 978-3-642-13025-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation