  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.
