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Information Retrieval with Implicitly Temporal Queries

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Time is an important factor that needs to be considered in information retrieval. Often queries raised to search engines may have temporal intention, or the search results that the user interests are time-dependent. In this paper, we deal with one type of temporal queries – implicitly temporal queries. Those queries do not contain explicitly temporal expressions, but the user’s information need is related to time. For a given query, we analyze its temporal intention and set up two linear combination models. One of them is based on the language modeling, and the other is based on the metric space model. Both of them consider both aspects of content and time to rank all the documents involved. Experiment results with the dataset that was used in the Temporal Information Access task of NTCIR-11 show that our approaches are effective.

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Notes

  1. 1.

    http://www.lemurproject.org/indri.php.

  2. 2.

    http://wiki.dbpedia.org/.

  3. 3.

    http://dbpedia.org/sparql.

  4. 4.

    https://sites.google.com/site/ntcirtemporalia/.

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Acknowledgement

This research has been partially supported by Natural Science Foundation of Jiangsu Province (number BK20171303).

Author information

Authors and Affiliations

  1. School of Computer Science, Jiangsu University, Zhenjiang, 212013, China

    Jingjing Wang & Shengli Wu

Authors
  1. Jingjing Wang
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  2. Shengli Wu
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Corresponding author

Correspondence to Shengli Wu .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Wang, J., Wu, S. (2017). Information Retrieval with Implicitly Temporal Queries. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

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