TY - GEN
T1 - A bayesian framework for automated dataset retrieval in geographic Information Systems
AU - Walker, Arron
AU - Pham, Binh
AU - Maeder, Anthony
PY - 2004/7/14
Y1 - 2004/7/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=3042753314&partnerID=8YFLogxK
U2 - 10.1109/MULMM.2004.1264978
DO - 10.1109/MULMM.2004.1264978
M3 - Conference contribution
AN - SCOPUS:3042753314
SN - 0769520847
SN - 9780769520841
T3 - Proceedings - 10th International Multimedia Modelling Conference, MMM 2004
SP - 138
EP - 144
BT - Proceedings - 10th International Multimedia Modelling Conference, MMM 2004
A2 - Chen, Y.-P.P.
T2 - Proceedings - 10th International Multimedia Modelling Conference, MMM 2004
Y2 - 5 January 2004 through 7 January 2004
ER -