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Improving Difficult Queries by Leveraging Clusters in Term Graph

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Information Retrieval Technology (AIRS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9460))

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Abstract

Term graphs, in which the nodes correspond to distinct lexical units (words or phrases) and the weighted edges represent semantic relatedness between those units, have been previously shown to be beneficial for ad-hoc IR. In this paper, we experimentally demonstrate that indiscriminate utilization of term graphs for query expansion limits their retrieval effectiveness. To address this deficiency, we propose to apply graph clustering to identify coherent structures in term graphs and utilize these structures to derive more precise query expansion language models. Experimental evaluation of the proposed methods using term association graphs derived from document collections and popular knowledge bases (ConceptNet and Wikipedia) on TREC datasets indicates that leveraging semantic structure in term graphs allows to improve the results of difficult queries through query expansion.

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Notes

  1. 1.

    http://wiki.dbpedia.org/Downloads39.

  2. 2.

    http://conceptnet5.media.mit.edu/downloads/20130917/associations.txt.gz.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Wayne State University, Detroit, MI, 48226, USA

    Rajul Anand & Alexander Kotov

Authors
  1. Rajul Anand
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  2. Alexander Kotov
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Corresponding author

Correspondence to Alexander Kotov .

Editor information

Editors and Affiliations

  1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

    Guido Zuccon

  2. Brisbane, Queensland, Australia

    Shlomo Geva

  3. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  4. RMIT University, Melbourne, Australia

    Falk Scholer

  5. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Aixin Sun

  6. Tianjin University, China

    Peng Zhang

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© 2015 Springer International Publishing Switzerland

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Anand, R., Kotov, A. (2015). Improving Difficult Queries by Leveraging Clusters in Term Graph. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds) Information Retrieval Technology. AIRS 2015. Lecture Notes in Computer Science(), vol 9460. Springer, Cham. https://doi.org/10.1007/978-3-319-28940-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-28940-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28939-7

  • Online ISBN: 978-3-319-28940-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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