RIO: Minimizing User Interaction in Debugging of Knowledge Bases

TL;DR

This paper introduces a reinforcement learning approach for interactive knowledge base debugging that adapts to uncertain meta-information, reducing user interactions and outperforming existing strategies.

Contribution

It presents a novel reinforcement learning method that minimizes the impact of unreliable meta-information in knowledge base debugging, improving efficiency without prior fault estimates.

Findings

Outperforms entropy-based strategies in reducing user interactions

Scalable and efficient on real-world knowledge bases

Adapts effectively to unreliable meta-information

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.