Relation prediction on knowledge graphs (KGs) is a key research topic. Dominant embedding-based methods mainly focus on the transductive setting and lack the inductive ability to generalize to new entities for inference. Existing methods for inductive reasoning mostly mine the connections between entities, i.e., relational paths, without considering the nature of head and tail entities contained in the relational context. This paper proposes a novel method that captures both connections between entities and the intrinsic nature of entities, by simultaneously aggregating RElational Paths and cOntext with a unified hieRarchical Transformer framework, namely REPORT. REPORT relies solely on relation semantics and can naturally generalize to the fully-inductive setting, where KGs for training and inference have no common entities. In the experiments, REPORT performs consistently better than all baselines on almost all the eight version subsets of two fully-inductive datasets. Moreover. REPORT is interpretable by providing each element's contribution to the prediction results.

翻译：知识图谱上的关系预测是一个关键研究课题。主流的基于嵌入的方法主要关注直推式设定，缺乏对未知实体进行推理的归纳能力。现有归纳推理方法大多挖掘实体间的连接（即关系路径），而未考虑关系上下文中包含的头尾实体本质属性。本文提出一种新颖方法，通过统一的分层Transformer框架（即REPORT）同时聚合关系路径与上下文，捕捉实体间连接及实体内在本质。REPORT仅依赖关系语义，可自然泛化至完全归纳设定——即训练与推理阶段的知识图谱无共同实体。实验表明，在两个完全归纳数据集的全部八个版本子集中，REPORT在几乎所有子集上均显著优于所有基线模型。此外，REPORT通过提供各元素对预测结果的贡献度，具备可解释性。