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Published Articles >> Table of Contents >> Abstract
10th International Multimedia Modelling Conference
p. 138
A Bayesian framework for automated dataset retrieval in Geographic Information Systems
Arron Walker, Queensland University of Technology
Binh Pham, Queensland University of Technology
Anthony Maeder, Queensland University of Technology
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MULMM.2004.1264978
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| Abstract |
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Existing Geographic Information Systems (GIS) are
intended for expert users and consequently, do not
provide any machine intelligence to assist users. This
paper presents a Bayesian framework that will
incorporate expert knowledge in order to retrieve all
relevant datasets given an initial user query. The
framework uses a spatial model that combines relational,
non-spatial and spatial data. This spatial model allows
efficient access of relational linkages for a Bayesian
network, and thus improves support for complex and
vague queries. The Bayesian network assigns causal
probabilities to these relational linkages in order to
define expert knowledge of related datasets in the GIS. In
addition, the framework will learn which datasets are
best suited for particular query input through feedback
supplied by the user.
This contribution will increase the performance and
efficiency of knowledge extraction from GIS by allowing
users to focus on interpreting data, instead of focusing on
finding which data is relevant to their analysis. The
initial user query can be vague and the framework will
still be capable of retrieving relevant datasets via the
linkages discovered in the Bayesian network.
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 Additional Information
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Citation:
Arron Walker, Binh Pham, Anthony Maeder,
"A Bayesian framework for automated dataset retrieval in Geographic Information Systems,"
mmm,
p. 138,
10th International Multimedia Modelling Conference,
2004
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