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Knowledge engineering, obtaining knowledge from experts and incorporating it into expert systems is difficult and time consuming. We suggest that these difficulties arise because experts never report on how they reach a decision, rather they justify why the decision is correct. These justifications vary markedly with the context in which they are required,(More)
A methodology for the modeling of large data sets is described which results in rule sets having minimal inter-rule interactions, and being simply maintained. An algorithm for developing such rule sets automatically is described and its efficacy shown with standard test data sets. Comparative studies of manual and automatic modeling of a data set of some(More)
Predicting people other people may like has recently become an important task in many online social networks. Traditional collabo-rative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have a dual role as both " users " and " items " , e.g., both initiating(More)
Ripple-Down Rules (RDR) is an approach to building knowledge-based systems (KBS) incrementally, while the KBS is in routine use. Domain experts build rules as a minor extension to their normal duties, and are able to keep refining rules as KBS requirements evolve. Commercial RDR systems are now used routinely in some Chemical Pathology laboratories to(More)
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(More)