Neural Knowledge Processing in Expert Systems

The knowledge base in expert systems usually contains different types of information which can be classified as explicit and implicit with respect to its representation. The explicit representation is based on a symbolic expression of human expert knowledge while the numerical data which require additional processing to be really understood represent the implicit knowledge. The rule-based systems and neural networks are typical examples of these different representation approaches. The main problem of rule-based systems is the knowledge acquisition which can be overcoming by learning and adaptation in neural networks. On the other hand, the neural implicit knowledge representation loses the capability to explain and justify the inference. Thus, the advantages and disadvantages of explicit and implicit knowledge representation in expert systems are complementary and we will first give a general comparison of both. Then we will discuss how to process the neural knowledge to embed it into..