Walk-based models have shown their unique advantages in knowledge graph (KG) reasoning by achieving state-of-the-art performance while allowing for explicit visualization of the decision sequence . However, the sparse reward signals offered by the KG during a traversal are often insufficient to guide a sophisticated  reinforcement learning (RL) model. An alternate approach to KG reasoning is using traditional symbolic methods (e.g., rule induction), which achieve high precision without learning but are hard to generalize due to the limitation of symbolic representation. In this paper, we propose to fuse these two paradigms to get the best of both worlds. Our method leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Due to the structure of symbolic rules with their entity variables, we can separate our walk-based agent into two sub-agents thus allowing for additional efficiency. Experiments on public datasets demonstrate that walk-based models can benefit from rule guidance significantly .
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 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 show that RuleGuider improves the performance of walk-based models without losing interpretability .
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unique
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state-of-the-art performance while allowing for explicit visualization of the decision sequence
decent performance while providing interpretable decisions
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walk-based
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to KG reasoning is using
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high precision without learning but are
good performance but can be
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to fuse these two paradigms to get the best of both worlds. Our method
RuleGuider, which
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Due to the structure of symbolic rules with their entity variables, we can separate our
Experiments on benchmark datasets show that RuleGuider improves the performance of
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agent into two sub-agents thus allowing for additional efficiency. Experiments on public datasets demonstrate that walk-based
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can benefit from rule guidance significantly
without losing interpretability