11 November 2008 Study on spatial knowledge representation and reasoning based on Bayesian networks
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Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 71461S (2008) https://doi.org/10.1117/12.813156
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Spatial information plays an essential role on the progress of science and technology, and has a profound impact on economic growth and society progress in the twenty-first century. Spatial knowledge representation and reasoning are very important for us to utilize spatial information. In this paper, a review is presented on spatial knowledge representation and reasoning. And then we propose a method of spatial knowledge representation and reasoning based on Bayesian networks. We focused on how to represent spatial relationship, spatial objects and spatial features by using Bayesian networks. Let spatial features (or spatial objects, spatial relationships) as variables or the nodes in Bayesian network, let directed edges present the relationships between spatial features, and the relevancy intensity can be regarded as confidence between the variables (the same as probability parameter in Bayesian network). Accordingly, the problem of spatial knowledge representation will be changed to the problem of learning Bayesian networks. The experimental results are given to verify the practical feasibility of the proposed methodology. Eventually, we conclude with a summary and a statement of future work.
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Jiejun Huang, Jiejun Huang, Peipei Qi, Peipei Qi, Yanyan Wu, Yanyan Wu, Yanbin Yuan, Yanbin Yuan, Fawang Ye, Fawang Ye, "Study on spatial knowledge representation and reasoning based on Bayesian networks", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71461S (11 November 2008); doi: 10.1117/12.813156; https://doi.org/10.1117/12.813156
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