A KNOWLEDGE BASE REFINEMENT METHOD BASED ON STRUCTURAL LEARNING OF NEURAL NETWORKS

Knowledge base refinement is necessary for knowledge acquisition in building expert systems. The KBANN （knowledge based artificial neural network） for knowledge base refinement has been proposed in literatures. The key limitation of KBANN is that there is no mechanism for changing the topology of the network. In this paper, a novel approach to knowledge base refinement based upon structural learning of neural networks is presented. In this approach, a set of rules are mapped into a neural network （i.e., the initial neural network）, and then this reformulated knowledge is refined using structural learning （i.e., the initial neural network is trained by structural learning algorithm and a set of training examples）. Finally, the refined rules are extracted from the trained neural network. To accomplish the topology change of the initial neural network, structural learning algorithms based on dynamic node creation and network pruning are used in this approach. Many simulation experiments have proved the effect of this approach.