Incorporating Cluster-Based Relationships in Web Rule Language

In the course of analyzing a wide variety of knowledge-based systems, Pragati has found many types of cluster-based relationships that can enable analysts and developers to comprehend, maintain, and reuse such systems more effectively. Pragati’s cluster-based analysis of numerous knowledge-based systems through its Multi-ViewPoint Clustering Analysis (MVP-CA) Tool [Mehrotra & Bobrovnikoff 2002] has demonstrated that clustering can expose a wide variety of relationships between formally represented concepts. These include templatization and refactoring opportunities, exposition of idioms, such as inverse rules, idempotent rules, and potential inter-system mappings.