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arXiv · 1302.2465

RIO: Minimizing User Interaction in Debugging of Knowledge Bases

Patrick Rodler·Kostyantyn Shchekotykhin·Philipp Fleiss·Gerhard Friedrich
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Abstract

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.

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Patrick Rodler, Kostyantyn Shchekotykhin, Philipp Fleiss, Gerhard Friedrich. 2013-02-11. RIO: Minimizing User Interaction in Debugging of Knowledge Bases. https://arxiv.org/abs/1302.2465

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