In this work, we present REGNUM, a system that enriches the body of the rules mined on a knowledge graph with atoms involving numerical predicates whose values are constrained by specified intervals. The intervals are obtained using supervised binning techniques with the objective of increasing the confidence of the rules provided by the rule mining technique. Our experimental results demonstrate that the rules enriched with numerical predicates have a higher overall quality and are better suited for the knowledge graph completion task.