A Semantic Decision Table (SDT) provides a means to capture and examine decision makers' concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner. One challenge SDT faces is to organize decision resources represented in a tabular format based on the user's needs at different levels. It is important to make it self organized and automatically reorganized when the requirements are updated. This paper describes the ongoing research on SDT and its tool that supports the self organizations and automatic reorganization of decision tables. We argue that simplicity, precision, and flexibility are the key issues to respond to the paper challenge. We propose a novel combination of the principles of Decision Support and Database Modeling, together with the modern technologies in Ontology Engineering, in the adaptive self-organization and automatic reorganization procedures (SOAR).
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