RIO: Minimizing User Interaction in Debugging of Knowledge Bases
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.
Cite
@article{arxiv.1302.2465,
title = {RIO: Minimizing User Interaction in Debugging of Knowledge Bases},
author = {Patrick Rodler and Kostyantyn Shchekotykhin and Philipp Fleiss and Gerhard Friedrich},
journal= {arXiv preprint arXiv:1302.2465},
year = {2013}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1209.3734