Treating Anomalies in Rule bases associated to Ontologies

Intelligent systems knowledge bases are grounded on ontologies and their associated rules. As intelligent systems act in dynamic and unpredictable environments, their knowledge bases need to be updated. Inevitable anomalies may arise and disturb the systems’ performance. Thus, rule bases verification became an indispensable task. In this paper, we propose an approach called TARO (Treating Anomalies in Rule bases associated to Ontologies). Our solution is structured in two steps: first, we extract dependency relationships between rules, then we use these relationships to identify and resolve anomalies. We developed a working prototype of our proposal which we named RB-Verif. Our approach’s effectiveness was demonstrated by the experiments carried out.