RIO: minimizing user interaction in debugging of aligned ontologies

Efficient ontology debugging is a cornerstone for many activities in the context of the Semantic Web, especially when automatic tools produce (parts of) ontologies such as in the field of ontology matching. The best currently known interactive debugging systems rely upon meta information in terms of fault probabilities, which can speed up the debugging procedure in the good case, but can also have negative impact in the bad case. Unfortunately, assessment of meta information is only possible a-posteriori. Hence, as long as the actual fault is unknown, there is always some risk of suboptimal interactive diagnoses discrimination. As an alternative, one might prefer to rely on a no-risk strategy. In this case, however, possibly well-chosen meta information cannot be exploited, resulting again in inefficient debugging actions. In this work we present a reinforcement learning strategy that continuously adapts its behavior depending on the performance achieved and minimizes the risk of using low-quality meta information. Therefore, this method is suitable for application scenarios where reliable a-priori fault estimates are difficult to obtain. Using faulty ontologies produced by ontology matchers, we show that the proposed strategy outperforms both active learning and no-risk approaches on average w.r.t. required amount of user interaction.