Sever, HayriGorur, A. KadirTolun, Mehmet R.2026-04-032026-04-0320079780769530321https://hdl.handle.net/20.500.12416/16042https://doi.org/10.1109/GrC.2007.56Organizational memory in today's business world forms basis for organizational learning, which is the ability of an organization to gain insight and understanding from experience through experimentation, observation, analysis, and a willingness to examine both successes and failures. This basically requires consideration of different aspects of knowledge that may reside on top of a conventional information management system. Of them, representation, retrieval and production issues of meta patterns constitute to the main theme of this article. Particularly we are interested in a formal approach to handle rough concepts. We utilize rough classifiers to propose a preliminary framework based on minimal term sets with p-norms to extract meta patterns. We describe a relational rule induction approach, which is called rila. Experimental results are provided on the mutagenesis, and the KDD Cup 2001 genes data sets.eninfo:eu-repo/semantics/closedAccessRough SetsP-Norm RetrievalOptimistic Estimate PruningRule Selection StrategiesClassifierRelational Rule InductionLogicConference Object10.1109/GrC.2007.56