Sparse Knowledge Graphs (KGs) are commonly encountered in real-world applications, where knowledge is often incomplete or limited. Sparse KG reasoning, the task of inferring missing knowledge over sparse KGs, is inherently challenging due to the scarcity of knowledge and the difficulty of capturing relational patterns in sparse scenarios. Among all sparse KG reasoning methods, path-based ones have attracted plenty of attention due to their interpretability. Existing path-based methods typically rely on computationally intensive random walks to collect paths, producing paths of variable quality. Additionally, these methods fail to leverage the structured nature of graphs by treating paths independently. To address these shortcomings, we propose a Structural and Probabilistic framework named StruProKGR, tailored for efficient and interpretable reasoning on sparse KGs. StruProKGR utilizes a distance-guided path collection mechanism to significantly reduce computational costs while exploring more relevant paths. It further enhances the reasoning process by incorporating structural information through probabilistic path aggregation, which prioritizes paths that reinforce each other. Extensive experiments on five sparse KG reasoning benchmarks reveal that StruProKGR surpasses existing path-based methods in both effectiveness and efficiency, providing an effective, efficient, and interpretable solution for sparse KG reasoning.

翻译：稀疏知识图谱（KGs）在现实应用中普遍存在，其中知识往往不完整或有限。稀疏知识图谱推理，即在稀疏知识图谱上推断缺失知识的任务，由于知识稀缺性以及在稀疏场景中捕捉关系模式的困难，本质上具有挑战性。在所有稀疏知识图谱推理方法中，基于路径的方法因其可解释性而备受关注。现有的基于路径方法通常依赖于计算密集的随机游走来收集路径，产生质量参差不齐的路径。此外，这些方法未能利用图的结构化特性，将路径独立处理。为克服这些不足，我们提出了一种名为StruProKGR的结构化与概率框架，专为稀疏知识图谱的高效且可解释推理而设计。StruProKGR采用距离引导的路径收集机制，在探索更多相关路径的同时显著降低计算成本。它进一步通过概率路径聚合融入结构信息，优先考虑相互增强的路径，从而提升推理过程。在五个稀疏知识图谱推理基准上的大量实验表明，StruProKGR在效果和效率上均超越现有基于路径的方法，为稀疏知识图谱推理提供了一个有效、高效且可解释的解决方案。