Expert System Based On Knowledge Extraction From A GIS
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abstract
Expert systems (ES) have been shown to be useful in many areas of natural resource and environmental impact studies. However, the major obstacle in the development of expert system is difficult to extract expert's knowledge into a knowledge base. An alternative approach that can overcome the obstacle is to extract domain knowledge from information system by machine learning. This study is the first experiment of knowledge extraction from geographic information system (KEGIS). Major effort in this study is to develop a landuse expert system with a knowledge base that is generated by learning from sample data of a geographic information system (GIS). In this study, 154 sample areas were selected from Wongnute County, Inner Mongolia, for knowledge base extraction. With the landuse knowledge base, an inference engine, and a user interface, a landuse expert system has been constructed for landuse consulting. In an accuracy test, The landuse expert system can provide 76% of correct suggestions. This result showed that the knowledge base created by KEGIS can closely represent the 'real world'.
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[Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium
