Whether carefully engineered or automatically learned, ontologies may still contain modeling errors. The problem of repairing Description Logic (DL) ontologies has been extensively studied, with a primary focus on minimizing information loss. A given ontology may have many different repairs that minimize information loss, and thus domain knowledge is needed to find the one that is correct in the given application domain. Consequently, interactive approaches have been proposed that involve users in the repair process, with the aim of reducing the number of user decisions required to obtain a valid repair. These methods usually assume that users are always able to make definitive decisions. However, users may face situations where the semantics of axioms alone are insufficient for them to determine whether an axiom should be retained or removed. In this work, we introduce an interactive repair approach that not only guides users through the exponential space of possible repairs while minimizing the decisions required, but also provides support in cases of uncertainty. Our approach augments the repair process with several decision-support features, including information about important entailments, distance to reachable repairs, and structural differences in the class hierarchy. We have implemented this approach both as a standalone command-line tool and as a web-based interactive visualization prototype that provides analytical capabilities not easily achievable otherwise.