RIO: Minimizing User Interaction in Debugging of Knowledge Bases
  The best currently known interactive debugging systems rely upon some
meta-information in terms of fault probabilities in order to improve their
efficiency. However, misleading meta information might result in a dramatic
decrease of the performance and its assessment is only possible a-posteriori.
Consequently, as long as the actual fault is unknown, there is always some risk
of suboptimal interactions. 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
prior fault estimates are difficult to obtain. Using diverse real-world
knowledge bases, we show that the proposed interactive query strategy is
scalable, features decent reaction time, and outperforms both entropy-based and
no-risk strategies on average w.r.t. required amount of user interaction.
