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The contribution of this paper is threefold: It substantially extends Ripple Down Rules, a proven eeective method for building large knowledge bases without a knowledge engineer. Furthermore, we propose to develop highly eeective heuristics searchers for combinatorial problems by a knowledge acquisition approach to acquire human search knowledge. Finally,(More)
The aim of our work is to provide support for reading (or skimming) scientific papers. In this paper we report on the task to identify concepts or terms with positive attributions in scientific papers. This task is challenging as it requires the analysis of the relationship between a concept or term and its sentiment expression. Furthermore, the context of(More)
We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients(More)
This paper introduces a new method for the rapid development of complex rule bases involving cue phrases for the purpose of classifying text segments. The method is based on Ripple-Down Rules, a knowledge acquisition method that proved very successful in practice for building medical expert systems and does not require a knowledge engineer. We implemented(More)
The development of highly eeective heuristics for search problems is a diicult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have introspective access to that knowledge , their explanations of actual search considerations seems very valuable in(More)