Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications. To alleviate this challenge, we present a novel framework, LR-GCN, that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC. The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller. The reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges, explicitly compositing long-range dependencies into the predictor. This step also plays an essential role in densifying KGs, effectively alleviating the sparse issue. Furthermore, the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the predictor. These two components are jointly optimized using a well-designed variational EM algorithm. Extensive experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.

翻译：稀疏知识图谱（KG）场景对以往的知识图谱补全（KGC）方法提出了挑战，即补全性能会随着图谱稀疏性的增加而快速下降。由于稀疏知识图谱在实际应用中的广泛存在，这一问题进一步加剧。为缓解这一挑战，我们提出了一种新颖的框架LR-GCN，该框架能够自动捕捉实体间有价值的长距离依赖关系，以补充不足的结构特征，并提炼逻辑推理知识用于稀疏KGC任务。所提出的方法包含两个主要组件：一个基于GNN的预测器和一个推理路径蒸馏器。推理路径蒸馏器探索推理路径等高阶图结构，并将其编码为富含语义的边，显式地将长距离依赖关系组合到预测器中。这一步在稠密化知识图谱方面也发挥着关键作用，有效缓解了稀疏性问题。此外，路径蒸馏器进一步从这些挖掘出的推理路径中提炼逻辑推理知识，并将其注入预测器。这两个组件通过精心设计的变分EM算法进行联合优化。在四个稀疏基准数据集上的广泛实验与分析验证了我们所提出方法的有效性。