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项关键研究课题。主流基于嵌入的方法主要关注直推式设置，缺乏归纳推理能力以泛化至新实体。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头尾实体的内在特性。本文提出一种新颖方法，通过统一层级Transformer框架同时聚合关系路径与上下文（REPORT），从而捕捉实体间的连接及其本质特征。REPORT仅依赖关系语义，能自然泛化至完全归纳设置——即训练与推理的知识图谱无共同实体。实验表明，REPORT在两个完全归纳数据集的全部八个版本子集上几乎均优于所有基线方法。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。