Document (#27755)

Author

Campos, L.M.
Fernez-Luna, J.M.
Huste, J.

Title

Managing documents with Bayesian belief networks : a brief survey of applications and models

Source

Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas

Imprint

Würzburg : Ergon Verlag

Year

2003

Pages

S.443-449

Series

Advances in knowledge organization; vol.8

Abstract

Belief networks are very appropriate tools to deal with uncertainty. They have been successfully applied to different environments. Information Retrieval has been one of these fields, where this propery (uncertainty) is a very important feature, which has not been well managed by the classical models. In this paper, we present a brief overview of the different applications found in the specialised literature where these graphical models have been used to solve specific problems or to design complete Information Retrieval Models. 1. Introduction The entire Information Retrieval (IR) cycle may be classified as an uncertain process because (Turtle & Croft, 1997): (1) The construction of the representations of documents and queries gives as a result incomplete characterisations, in the form of a set of terms. (2) The submitted query is just a vague description of the users' information need. (3) The computation of the relevance degree of each document with respect to the query inherits the previous uncertainty sources, but also presents the problems that can cause the different representations that a concept may have, as well as, that these concepts are not independent among them. Probabilistic models (Crestani et al., 1998) tried to solve these problems although they have some limitations to overcome them. The development of Belief Networks and their applications to actual problems, with good results, caused that several researchers in the field of IR focused their attention an them. They realised that these kind of networks could be adequate models to be employed in IR, specially designed to work with a high performance in environments in which uncertainty is a very important feature, as the case of IR is. Also, because they can properly represent the relationships among variables.

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4. Donahue, J.; Hendricks, L.A.; Guadarrama, S.; Rohrbach, M.; Venugopalan, S.; Saenko, K.; Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description (2014) 0.27

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5. Ford, N.: Cognitive styles and virtual environments (2000) 0.26

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