Fusion Methodologies in Crisis Management

pp 87-104

Uncertainty Representations for Information Retrieval with Missing Data

  • Anne-Laure JousselmeAffiliated withNATO Science and Technology Organization, Centre for Maritime Research and Experimentation (CMRE) Email author 
  • , Patrick MaupinAffiliated withCommand, Control and Intelligence (C2I) Section, Defence R & D Canada - Valcartier

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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.