Intermediate Concept Discovery in Ripple-Down Rule Knowledge Bases

In this paper we investigate how Ripple Down Rules (RDR) knowledge-based systems (KBS) can be reorganized and intermediate concepts discovered. The experiment is based on a real-world multiple-classification RDR knowledge base (KB) with 3710 rules and 2211 cornerstone cases used for interpreting lipid results in chemical pathology. RDR knowledge acquisition can start with a minimal ontology and adding a rule is simple and rapid. On the other hand there is the drawback of possible repetition, redundancy and lack of intermediate concepts. This paper demonstrates how repetition and redundancy can be elimitated by reorganising RDR and how intermediate concepts can be discovered.