This paper presents an approach for rapid knowledge capture using subgroup-discovery techniques. The method enables the acquisition of scoring rules - a knowledge representation that is easy to understand and to maintain. Furthermore, the method features an incremental refinement step that can be applied for fine-tuning of the learned relations. We provide a case study demonstrating the applicability of the presented method using a knowledge base from the biological domain.

This paper presents a collaborative knowledge engineering approach based on the widespread wiki technique.The interface of a standard wiki system is extended to allow for the capture, the maintenance and the use of knowledge systems.

User-centered consultation systems can be viewed as systems with trust in the competence of the user, whereas conventional knowledge systems are often based on some mistrust. By offering a variety of formal and informal diagnostic services mediated by flexible agents, the user solves his or her problem by asking the agents for specific information complementary to his or her knowledge. The development process is flexible enough to build minimalist Wiki-like informal systems very quickly, which can be incrementally refined and formalized in a distributed manner. The approach is demonstrated with two case studies.

Although a lot of work in the field of knowledge acquisition has been done, the manual development of diagnostic knowledge systems by domain experts still is a very complex task. In this paper we will present an incremental approach for building diagnostic systems based on set-covering models. We start with a simple model describing the coarse structure between diagnoses and findings. Subsequently, this simple model can be euhanced by similarities, weights and probabilities to increase the accuracy of the knowledge and the resulting system. We will also show how these static set-covering models can be combined with dynamic set-covering models including higher level knowledge about causation effects. We will motivate how dynamic set-covering models can be used for implementing diagnostic systems including therapy effects. Finally, we report on two practical applications dealing with set-covering models from the geo-ecological and from the medical domain, respectively, that we have implemented.