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ECAI
2000
Springer

Learning Classification taxonomies from a classification knowledge based system

10 years 2 months ago
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 ...
Hendra Suryanto, Paul Compton
Added 02 Aug 2010
Updated 02 Aug 2010
Type Conference
Year 2000
Where ECAI
Authors Hendra Suryanto, Paul Compton
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