Visualizing Logical Dependencies in SWRL Rule Bases

Rule bases are common in many business rule applications, clinical decision support programs, and other types of intelligent systems. As the size of the rule bases grows and the interrelationships between rules become more complex, understanding dependencies among rules can be quite difficult. To address this challenge, we propose a novel approach for modeling logical dependencies among rules and for discovering patterns based on these dependencies. Our method uses rules bases written in the Semantic Web Rule Language (SWRL); we exploit SWRL’s logical relationship with OWL to incorporate these semantics in our analysis. We couple this analysis with visualization techniques that create a rule dependency graph. We group nodes into layers based on their dependencies and cluster nodes within a layer if they have similar dependencies. We have evaluated our approach by applying it to two independently developed, publicly available ontologies containing SWRL rules. We show how our analysis and visualization approach can allow users to quickly examine patterns of logical relationships in such rule bases.