Gangrong Jiang


2020

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Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
Deren Lei | Gangrong Jiang | Xiaotao Gu | Kexuan Sun | Yuning Mao | Xiang Ren
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets shows that RuleGuider clearly improves the performance of walk-based models without losing interpretability.