Generalising Ripple-Down Rules

Compton, Paul; Richards, Debbie

doi:10.1007/3-540-39967-4_29

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…The use of a simple, ‘one size fits all’, generic KA approach is quite unusual and contrary to much mainstream KBS research, where knowledge level modelling has been the driver for KA. This notion of generalized RDR, while never given a name, has been specifically explored in Compton and Richards (2000) and described in a provisional patent lodged in 1999. Generalized RDR seeks to handle construction type problems, and thus classification problems which can be seen to be a simple construction problem that can be solved in one inference cycle, where the solution has a finite number of components and relationships between components without the need for searching, backtracking or changing parts of the solution already developed.…”

Section: Variations Extensions and Implementationsmentioning

confidence: 99%

Two decades of Ripple Down Rules research

Richards

2009

The Knowledge Engineering Review

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Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to maintenance as it was expected that extensive upfront domain analysis involving a highly trained specialist, the knowledge engineer, and the time-poor domain expert would produce a complete model capturing what was in the expert’s head. The ever-changing, contextual and embrained nature of knowledge were not a part of the philosophy upon which they were based. RDR was a paradigm shift, which made knowledge acquisition and maintenance one and the same thing by incrementally acquiring knowledge as domain experts directly interacted with naturally occurring cases in their domain. Cases played an integral part of the acquisition process by motivating the capture of new knowledge, framing the context in which new knowledge would apply and ensuring that previously correctly classified cases remained so by requiring that the classification of the new case distinguish it from the system’s classification and be justified by features of the new case. RDR has moved beyond its first representation which handled single classification tasks within the domain of pathology to support multiple conclusions across a wide range of domains such as help-desk support, email classification and RoboCup and problem types including configuration, simulation, planning and natural language processing. This paper reviews the history of RDR research over the past two decades with a view to its future.

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Section: Variations Extensions and Implementationsmentioning

confidence: 99%

Two decades of Ripple Down Rules research

Richards

2009

The Knowledge Engineering Review

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“…He/she can not properly maintain the knowledge base without help from knowledge engineers. The Multiple Classification Ripple-Down Rules (MCRDR) -an incremental knowledge acquisition method -is proposed to overcome this problem and is based on the maintenance experience of a real world medical expert system, called GARVAN-ES1 [11,12]. We use the MCRDR method to construct our filtering system and in the next section we provide a detailed explanation.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Web content filtering based on user's knowledge

Churcharoenkrung

Kim

Kang

2005

International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II

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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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Generalising Ripple-Down Rules

Cited by 10 publications

References 4 publications

Two decades of Ripple Down Rules research

Two decades of Ripple Down Rules research

Dynamic Web content filtering based on user's knowledge

Discovery of Class Relations in Exception Structured Knowledge Bases

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