Spatial Bayesian learning algorithms for geographic information retrieval

Walker, Arron R., Pham, Binh L., & Moody, Miles P. (2005) Spatial Bayesian learning algorithms for geographic information retrieval. In 13th annual ACM international workshop on Geographic information systems, November 2005, Bremen, Germany.

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An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Geographic Information Retrieval (GIR). Typically, GIR looks at the problem of retrieving GIS datasets on a theme by theme basis. However in practice, themes are generally not analysed in isolation. More often than not multiple themes are required to create a map for a particular analysis task. To do this using the current GIR techniques, each theme is retrieved one by one using traditional retrieval methods and manually added to the map. To automate map creation the traditional GIR paradigm of matching a query to a single theme type must be extended to include discovering relationships between different theme types.Bayesian Inference networks can and have recently been adapted to provide a theme to theme relevance ranking scheme which can be used to automate map creation [2]. The use of Bayesian inference for GIR relies on a manually created Bayesian network. The Bayesian network contains causal probability relationships between spatial themes. The next step in using Bayesian Inference for GIR is to develop algorithms to automatically create a Bayesian network from historical data. This paper discusses a process to utilize conventional Bayesian learning algorithms in GIR. In addition, it proposes three spatial learning Bayesian network algorithms that incorporate spatial relationships between themes into the learning process. The resulting Bayesian networks were loaded into an inference engine that was used to retrieve all relevant themes given a test set of user queries. The performance of the spatial Bayesian learning algorithms were evaluated and compared to performance of conventional non-spatial Bayesian learning algorithms.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.

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ID Code: 3637
Item Type: Conference Paper
Refereed: Yes
Keywords: geographic information retrieval, geographic information system, information retrieval, learning Bayesian networks, spatial Bayesian learning
DOI: 10.1145/1097064.1097080
ISBN: 1595931465
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > QUT Faculties and Divisions > Division of Research and Commercialisation
Copyright Owner: Copyright 2005 ACM Press
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 07 Mar 2006 00:00
Last Modified: 29 Feb 2012 13:13

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