RIO: Minimizing User Interaction in Ontology Debugging
  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 some meta information in terms of fault
probabilities, which can speed up the debugging procedure in the good case, but
can also have negative impact on the performance in the bad case. The problem
is that assessment of the meta information is only possible a-posteriori.
Consequently, 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 tool which pursues 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 problematic
ontologies in the field of ontology matching, we show that the proposed
risk-aware query strategy outperforms both active learning approaches and
no-risk strategies on average in terms of required amount of user interaction.
