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Moving towards Adaptive Search in Digital Libraries

  • Udo Kruschwitz
  • M-Dyaa Albakour
  • Jinzhong Niu
  • Johannes Leveling
  • Nikolaos Nanas
  • Yunhyong Kim
  • Dawei Song
  • Maria Fasli
  • Anne De Roeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6699)

Abstract

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour.

Keywords

Association Rule Domain Model Digital Library Implicit Feedback Query Suggestion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Udo Kruschwitz
    • 1
  • M-Dyaa Albakour
    • 1
  • Jinzhong Niu
    • 1
  • Johannes Leveling
    • 2
  • Nikolaos Nanas
    • 3
  • Yunhyong Kim
    • 4
  • Dawei Song
    • 4
  • Maria Fasli
    • 1
  • Anne De Roeck
    • 5
  1. 1.University of EssexColchesterUK
  2. 2.Dublin City UniversityDublinIreland
  3. 3.Centre for Research and TechnologyThessalyGreece
  4. 4.Robert Gordon UniversityAberdeenUK
  5. 5.Open UniversityMilton KeynesUK

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