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A FRAMEWORK FOR INCREMENTAL KNOWLEDGE BASE UPDATE FROM ADDITIONAL DATA COVERAGES

The paper describes a methodology for the incremental addition of remotely sensed data layers to a rule-based knowledge model. In particular, it reports on the effects of the addition of an Electromagnetic (EM31) layer to an existing GIS database consisting of groundwater depth, soil type, geology, slope, elevation, and the seven (7) spectral bands of Landsat TM. A field survey recharge coverage is added to a GIS database for three (3) classes of recharge, low medium and high potential. This is supplemented by a partial EM31 coverage. The resulting database is input to a inductive learning program and a decision tree classifier is produced. The experiment was performed by computer scientists without any prior knowledge of the impact of EM31 on the classification task. The intent of the experiment was to discover: (i) whether the resultant inductive decision tree encapsulates any significant attribute value discriminators in the new EM31 coverage layer; (ii) whether the data contained within existing coverages already informationally subsumes the new EM31 coverage; (iii) whether any conclusions were forthcoming about the classification task without prior knowledge of the nature and affect of the EM31 coverage in the domain classification task.