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RAF: An Activation Framework for Refining Similarity Queries Using Learning Techniques

  • Yiming Ma
  • Sharad Mehrotra
  • Dawit Yimam Seid
  • Qi Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)

Abstract

In numerous applications that deal with similarity search, a user may not have an exact specification of his information need and/or may not be able to formulate a query that exactly captures his notion of similarity. A promising approach to mitigate this problem is to enable the user to submit a rough approximation of the desired query and use relevance feedback on retrieved objects to refine the query. In this paper, we explore such a refinement strategy for a general class of structured similarity queries. Our approach casts the refinement problem as that of learning concepts using the tuples on which the user provides feedback as a labeled training set. Under this setup, similarity query refinement consists of two learning tasks: learning the structure of the query and learning the relative importance of query components. The paper develops machine learning approaches suitable for the two learning tasks. The primary contribution of the paper is the Refinement Activation Framework (RAF) that decides when each learner is invoked. Experimental analysis over many real life datasets shows that our strategy significantly outperforms existing approaches in terms of retrieval quality.

Keywords

Relevance Feedback Original Query Initial Query Similarity Query Activation Framework 
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 2006

Authors and Affiliations

  • Yiming Ma
    • 1
  • Sharad Mehrotra
    • 1
  • Dawit Yimam Seid
    • 1
  • Qi Zhong
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaIrvineUSA

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