Improving Retrieval Results with Discipline-Specific Query Expansion

  • Thomas Lüke
  • Philipp Schaer
  • Philipp Mayr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7489)


Choosing the right terms to describe an information need is becoming more difficult as the amount of available information increases. Search-Term-Recommendation (STR) systems can help to overcome these problems. This paper evaluates the benefits that may be gained from the use of STRs in Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and one giving general recommendations, and compare the retrieval performance of these STRs. The main findings are: (1) QE with specific STRs leads to significantly better results than QE with a general STR, (2) QE with specific STRs selected by a heuristic mechanism of topic classification leads to better results than the general STR, however (3) selecting the best matching specific STR in an automatic way is a major challenge of this process.


Term Suggestion Information Retrieval Thesaurus Query Expansion Digital Libraries Search Term Recommendation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Thomas Lüke
    • 1
  • Philipp Schaer
    • 1
  • Philipp Mayr
    • 1
  1. 1.GESIS – Leibniz Institute for the Social SciencesCologneGermany

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