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Uncertainty Representations for Information Retrieval with Missing Data

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Fusion Methodologies in Crisis Management

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Abstract

Retrieving items such as similar past events, or vessels with a specific characteristic of interest, is a critical task for crisis management support. The problem of information retrieval from incomplete databases is addressed in this paper. In particular, we assess the impact of the uncertainty representation about missing data for retrieving the corresponding items. After a brief survey on the problem of missing data with an emphasis on the information retrieval application, we propose a novel approach for retrieving records with missing data. The general idea of the proposed data-driven approach is to model the uncertainty pertaining to this missing data. We chose the general model of belief functions as it encompasses as special cases both classical set and probability models. Several uncertainty models are then compared based on (1) an expressiveness criterion (non-specificity or randomness) and (2) objective measures of performance typical to the Information Retrieval domain. The results are illustrated on a real dataset and a simulation controlled missing data mechanism.

This work is an updated version of Jousselme and Maupin (2013).

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Notes

  1. 1.

    http://www.nctc.gov.

  2. 2.

    http://msi.nga.mil/NGAPortal/MSI.portal?_nfpb=true&_pageLabel=msi_portal_page_64.

  3. 3.

    http://www.nctc.gov.

  4. 4.

    http://www.geocommons.com.

  5. 5.

    Maritime Mobile Service Identity.

  6. 6.

    International Maritime Organization.

  7. 7.

    Descriptors are also called terms, features, attributes, etc.

  8. 8.

    The typology of uncertainty types referred here is the one of Klir and Yuan (1995), in which fuzziness is omitted. Note that randomness is called discord in Klir and Yuan (1995).

  9. 9.

    We used here equal weights.

  10. 10.

    http://eturwg.c4i.gmu.edu.

  11. 11.

    http://archive.ics.uci.edu/ml/index.html.

  12. 12.

    Among the series of results obtained for different values of Ï„ we selected these ones as they were amongst those with (1) a clear difference between the models and (2) good performances. Further results will be provided in an extended version of our work.

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Author information

Authors and Affiliations

  1. NATO Science and Technology Organization, Centre for Maritime Research and Experimentation (CMRE), Viale San Bartolomeo 400, 19126, La Spezia, Italy

    Anne-Laure Jousselme

  2. Command, Control and Intelligence (C2I) Section, Defence R & D Canada - Valcartier, Quebec, QC, Canada, G3J 1X5

    Patrick Maupin

Authors
  1. Anne-Laure Jousselme
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  2. Patrick Maupin
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Correspondence to Anne-Laure Jousselme .

Editor information

Editors and Affiliations

  1. State University of New York at Buffalo, Buffalo, New York, USA

    Galina Rogova

  2. State University of New Yorkat Buffalo, Buffalo, New York, USA

    Peter Scott

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Jousselme, AL., Maupin, P. (2016). Uncertainty Representations for Information Retrieval with Missing Data. In: Rogova, G., Scott, P. (eds) Fusion Methodologies in Crisis Management. Springer, Cham. https://doi.org/10.1007/978-3-319-22527-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-22527-2_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22526-5

  • Online ISBN: 978-3-319-22527-2

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