Inductive knowledge acquisition
نویسندگان
چکیده
At the present state of art, generating a rule base is one of the main challenges in the area of knowledge-based systems. The present work attempts to automate the parts of the process of knowledge acquisition by using neural networks with rule extraction techniques. This paper presents a methodology composed of four phases to generate a representation formalism based on quantiied rules of n-ary predicates, facts and type hierarchy. Predicate rules extracted from neural networks have been used successfully to initialise SHRUTI reasoning system. The automated knowledge base enables the greater explanatory capabilities by allowing user interaction. Moreover, empirical results demonstrate that these predicate rules extracted from neural networks have a high accuracy.
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