In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to empower retrieval systems, recommender systems, and question answering systems, generating a surge in research interest. Recently developed reasoning methods usually suffer from poor performance when applied to incomplete or sparse KGs, due to the lack of evidential paths that can reach target entities. To solve this problem, we propose a hybrid multi-hop reasoning model with reinforcement learning (RL) called SparKGR, which implements dynamic path completion and iterative rule guidance strategies to increase reasoning performance over sparse KGs. Firstly, the model dynamically completes the missing paths using rule guidance to augment the action space for the RL agent; this strategy effectively reduces the sparsity of KGs, thus increasing path search efficiency. Secondly, an iterative optimization of rule induction and fact inference is designed to incorporate global information from KGs to guide the RL agent exploration; this optimization iteratively improves overall training performance. We further evaluated the SparKGR model through different tasks on five real world datasets extracted from Freebase, Wikidata and NELL. The experimental results indicate that SparKGR outperforms state-of-the-art baseline models without losing interpretability.

近年来，基于知识图谱（KG）的推理已被广泛应用于增强检索系统、推荐系统和问答系统的能力，引起了研究兴趣的激增。由于缺乏可以到达目标实体的证据路径，最近开发的推理方法在应用于不完整或稀疏的 KG 时通常会表现不佳。为了解决这个问题，我们提出了一种名为SparKGR的带有强化学习 (RL) 的混合多跳推理模型，它实现了动态路径完成和迭代规则引导策略，以提高稀疏 KG 的推理性能。首先，该模型使用规则引导动态完成缺失的路径，以增加 RL 代理的动作空间；该策略有效地降低了 KG 的稀疏性，从而提高了路径搜索效率。其次，设计了规则归纳和事实推理的迭代优化，以结合来自 KG 的全局信息来指导 RL 代理探索；这种优化迭代地提高了整体训练性能。我们通过对从 Freebase、Wikidata 和 NELL 中提取的五个真实世界数据集的不同任务进一步评估了SparKGR模型。实验结果表明，SparKGR在不失去可解释性的情况下优于最先进的基线模型。