Improving Database Retrieval on the Web through Query Relaxation

  • Markus Pfuhl
  • Paul Alpar
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 37)


Offering database content to unknown Web users creates two problems. First, users need to know about its existence. Second, once they know that it exists, they need to be able to retrieve it. We concentrate on the latter task. The same problem occurs also within an organization but there at least the skilled users can use powerful tools like SQL to find any content within the database. Web interfaces to databases are relatively simple and restricted. Even a skilled user could not define complicated queries due to their limitations. Therefore, especially databases that should be accessed via the Web should offer more “intelligence”. We propose two features towards this goal. First, taxonomies should be built for selected attributes. Second, better query results should be offered by relaxing user queries based on the knowledge captured in the taxonomies. In this paper, we derive a method for query relaxation guided by the ideas of Bayesian inference. It helps to select the best attribute to relax the query in a retrieval step. The approach is applied to taxonomy-based attributes although it can be generalized to other types of attributes as well. The quality of the method is tested with data from an actual database offered on the Web.


Query Expansion News Item Structure Query Language Bayesian Decision Theory Relaxation Step 
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 2009

Authors and Affiliations

  • Markus Pfuhl
    • 1
  • Paul Alpar
    • 1
  1. 1.Institut für WirtschaftsinformatikPhilipps-Universität MarburgMarburg

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