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A Social Semantic Approach to Adaptive Query Expansion

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Web Information Systems and Technologies (WEBIST 2014)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 226))

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Abstract

Classic query expansion approaches are based on the use of two-dimensional co-occurrence matrices. In this paper, we propose the adoption of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as Delicious and StumbleUpon. The results of an in-depth experimental evaluation performed on real users show that our approach outperforms traditional techniques, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes.

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Notes

  1. 1.

    delicious.com.

  2. 2.

    stumbleupon.com.

  3. 3.

    wordnet.princeton.edu.

  4. 4.

    www.ontologyportal.org.

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Authors and Affiliations

  1. Department of Engineering, Artificial Intelligence Laboratory, Roma Tre University, Via della Vasca Navale, 79, 00146, Rome, Italy

    Claudio Biancalana, Fabio Gasparetti, Alessandro Micarelli & Giuseppe Sansonetti

Authors
  1. Claudio Biancalana
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  2. Fabio Gasparetti
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  3. Alessandro Micarelli
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  4. Giuseppe Sansonetti
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Corresponding author

Correspondence to Giuseppe Sansonetti .

Editor information

Editors and Affiliations

  1. University of Paris, Paris, Paris, France

    Valérie Monfort

  2. RWTH Aachen University, Aachen, Germany

    Karl-Heinz Krempels

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Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G. (2015). A Social Semantic Approach to Adaptive Query Expansion. In: Monfort, V., Krempels, KH. (eds) Web Information Systems and Technologies. WEBIST 2014. Lecture Notes in Business Information Processing, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-27030-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-27030-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27029-6

  • Online ISBN: 978-3-319-27030-2

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