One of the main problems in the design of intelligent systems is the socalled “knowledge engineering bottleneck”: human experts master their respective tasks, but are nevertheless unable to communicate their problem solving techniques in a way that would allow to directly transfer their expert knowledge into a computer program. The knowledge engineer is the mediator between man and machine in this process.
The “Knowledge Engineering” group concentrates on the acquisition of explicit, formalizable knowledge from sources that contain relevant information in implicit or not directly accessible form. Its methodological focus is on the use of techniques from machine learning and data mining for knowledge acquisition by analysis of existing data or text collections, by interaction with human experts, or by experimentation and simulation. The applicability of the employed methodologies ranges from cognitive science problems to industrial applications in the areas of data or web mining.
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