Skip to main content

Improving Information Retrieval by Concept-Based Ranking

  • Conference paper
Human Interaction with Machines
  • 388 Accesses

Abstract

With the Internet getting available to more and more people in the last decade and with the rapidly growing number of webpages the internet is a vast resource of information. Millions of people are using the internet to search for information every day. However, the search result is not satisfying in the case of an ambiguous search query. In this paper an algorithm for re-ranking pages according to concepts - the content of webpages — is proposed. This algorithm uses Association rules, a Data Mining technique, to derive concepts from webpages. The preliminary experiment shows a promising result compared with hyperlink-based ranking algorithms.

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

Access this chapter

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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Agrawal R, Srikant R (1994) Fast Algorithms for Mining Association Rules. Research Report RJ9839, IBM Almaden Research Center, San Jose, CA.

    Google Scholar 

  2. Brin S, Motwani R, Page L, Winograd T (1998) The PageRank citation ranking: Bringing order to the Web. Stanford CS Technical Report.

    Google Scholar 

  3. Buchholz M, Pflüger D, Poon J (2004) Application of Machine Learning Techniques to the Re-ranking of Search Results. KI 2004: Advances in Artificial Intelligence, 27th Annual German Conference on AI, KI 2004, Ulm, Germany, September 20–24, 2004, Proc.

    Google Scholar 

  4. Frank E, Gutwin C, Nevill-Manning CG, Paynter GW, Witten IH (1999) KEA: Practical Automatic Keyphrase Extraction. Proc DL’ 99, pp. 254–256.

    Google Scholar 

  5. Pirolli P, Pitkow J, Rao R: Silk from a Sow’s Ear (1996) Extracting Usable Structures from the Web. Proc of ACM SIGCHI’ 96, Vancouver, Canada, 118–125

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this paper

Cite this paper

Mehlitz, M., Li, F. (2006). Improving Information Retrieval by Concept-Based Ranking. In: Hommel, G., Huanye, S. (eds) Human Interaction with Machines. Springer, Dordrecht . https://doi.org/10.1007/1-4020-4043-1_18

Download citation

  • DOI: https://doi.org/10.1007/1-4020-4043-1_18

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-4042-9

  • Online ISBN: 978-1-4020-4043-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics