The automatic compression of multiple classification ripple down rule knowledge based systems: preliminary experiments

Suryanto, Hendra; Richards, Debbie; Compton, Paul

doi:10.1109/kes.1999.820155

Cited by 2 publications

(1 citation statement)

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“…Further, despite the random order in which cases are presented, and the likelihood of experts providing less that ideal rules, manually built RDR systems produce compact KBs [4], [11], [22]. Gaines has also generalized RDR to Exception Directed Acyclic Graphs (EDAGs).…”

Section: Ripple Down Rulesmentioning

confidence: 99%

Discovery of Class Relations in Exception Structured Knowledge Bases

Suryanto

Compton

2000

Lecture Notes in Computer Science

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Abstract. Knowledge-based systems (KBS) are not necessarily based on a well-defined ontologies. In particular it is possible to build very successful KBS for classification problems, but where the classes or conclusions are entered by experts as free-text sentences with little constraint on textual consistency and little systematic organisation of the conclusions. This paper investigates how relations between such 'classes' may be discovered from existing knowledge bases. We have based our approach on KBS built with Ripple Down Rules (RDR). RDR is a knowledge acquisition and knowledge maintenance method which allows KBS to be built very rapidly and simply by correcting errors, 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. It follows earlier work on using Formal Concept Analysis (FCA) to discover ontologies in RDR KBS.

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Section: Ripple Down Rulesmentioning

confidence: 99%