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Query expansion using pseudo relevance feedback on wikipedia

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Abstract

One of the major challenges in Web search pertains to the correct interpretation of users’ intent. Query Expansion is one of the well-known approaches for determining the intent of the user by addressing the vocabulary mismatch problem. A limitation of the current query expansion approaches is that the relations between the query terms and the expanded terms is limited. In this paper, we capture users’ intent through query expansion. We build on earlier work in the area by adopting a pseudo-relevance feedback approach; however, we advance the state of the art by proposing an approach for feature learning within the process of query expansion. In our work, we specifically consider the Wikipedia corpus as the feedback collection space and identify the best features within this context for term selection in two supervised and unsupervised models. We compare our work with state of the art query expansion techniques, the results of which show promising robustness and improved precision.

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Notes

  1. Wikipedia articles https://en.wikipedia.org/wiki/Obama_Family and https://en.wikipedia.org/wiki/Family_tree respectively.

  2. https://en.wikipedia.org/wiki/Hotel_California_(disambiguation)

  3. https://en.wikipedia.org/wiki/Land_mine_(disambiguation)

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Authors and Affiliations

  1. Ryerson University, Toronto, ON, Canada

    Andisheh Keikha & Ebrahim Bagheri

  2. Ferdowsi University of Mashhad, Mashhad, Iran

    Faezeh Ensan

Authors
  1. Andisheh Keikha
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  2. Faezeh Ensan
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  3. Ebrahim Bagheri
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Correspondence to Ebrahim Bagheri.

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Keikha, A., Ensan, F. & Bagheri, E. Query expansion using pseudo relevance feedback on wikipedia. J Intell Inf Syst 50, 455–478 (2018). https://doi.org/10.1007/s10844-017-0466-3

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  • DOI: https://doi.org/10.1007/s10844-017-0466-3

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