Learning Classification taxonomies from a classification knowledge based system

Knowledge-based systems (KBS) are not necessarily based on well-defined ontologies. In particular it is possible to build KBS for classification problems, where there is little constraint on how classes are organised and a class is expressed by the expert as a free text conclusion to a rule. This paper investigates how relations between such 'classes' may be discovered from existing knowledge bases, then investigates how to construct a model of these classes (an ontology) based on user-selected patterns in the class relations. We have applied our approach to KBS built with Ripple Down Rules (RDR) [1] RDR is a knowledge acquisition and knowledge maintenance methodology, which allows KBS to be built very rapidly and simply, but does not require a strong ontology. Our experimental results are based on a large real-world medical RDR KBS. The motivation for our work is to allow an ontology in a KBS to 'emerge' during development, rather than requiring the ontology to be established prior to the development of the KBS.

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