Abstract
Shorter product cycles, lower prices of products, and the production of goods that are tailored to the customers needs made knowledge based product configuration systems a great success of AI technology. However, configuration knowledge bases tend to become large and complex. Therefore, knowledge acquisition and maintenance are crucial phases in the life-cycle of a configuration system. We will show how to meet this challenge by extending a standard design language from the area of Software Engineering with classical description concepts for expressing configuration knowledge. We automatically translate this graphical depiction into logical sentences which can be exploited by a general inference engine to solve the configuration task. In order to cope with usability restrictions of diagrammatic notations for large applications, we introduce the usage of contextual diagrams. This mechanism makes the conceptual model more readable and understandable and supports intuitively the acquisition of functional configuration knowledge.