Metric Spaces for Temporal Information Retrieval

  • Matteo Brucato
  • Danilo Montesi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


Documents and queries are rich in temporal features, both at the meta-level and at the content-level. We exploit this information to define temporal scope similarities between documents and queries in metric spaces. Our experiments show that the proposed metrics can be very effective for modeling the relevance for different search tasks, and provide insights into an inherent asymmetry in temporal query semantics. Moreover, we propose a simple ranking model that combines the temporal scope similarity with traditional keyword similarities. We experimentally show that it is not worse than traditional keyword-based rankings for non-temporal queries, and that it improves the overall effectiveness for time-based queries.


Information Retrieval Temporal Expression Mean Average Precision Vector Space Model Ranking Model 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Matteo Brucato
    • 1
  • Danilo Montesi
    • 2
  1. 1.School of Computer ScienceUniversity of MassachusettsAmherstUSA
  2. 2.Department of Computer Science and EngineeringUniversity of BolognaItaly

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