A Textual Subgroup Mining Approach for Rapid ARD+ Model Capture

Manual knowledge acquisition is usually a costly and time-consuming process. Automatic knowledge acquisition methods can then significantly support the knowledge engineer. In this paper, we propose an approach for rapid knowledge capture. The methodology is based on textual subgroup mining in order to discover dependencies for rule prototyping.