Abstract
Rule-based knowledge bases are constantly increasing in volume, thus the knowledge stored as a set of rules is getting progressively more complex and when rules are not organized into any structure, the system is inefficient. In the author’s opinion, modification of both the knowledge base structure and inference algorithms lead to improve the efficiency of the inference process. Rules partition enables reducing significantly the percentage of the knowledge base analysed during the inference process. The form of the group’s representative plays an important role in the efficiency of the inference process. The good performance of this approach is shown through an extensive experimental study carried out on a collection of real knoswledge bases.
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