A Textual Subgroup Mining Approach for Rapid ARD+ Model Capture

Manual knowledge acquisition is usually a costly and timeconsuming 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.

• Type: Journal
• Year: 2009
• Where: FLAIRS
• Authors: Martin Atzmueller, Grzegorz J. Nalepa
• Keywords: Artificial Intelligence, Automatic Knowledge Acquisition, FLAIRS 2009, Knowledge Acquisition, Manual Knowledge Acquisition