  Relation prediction in knowledge graphs is dominated by embedding based
methods which mainly focus on the transductive setting. Unfortunately, they are
not able to handle inductive learning where unseen entities and relations are
present and cannot take advantage of prior knowledge. Furthermore, their
inference process is not easily explainable. In this work, we propose an
all-in-one solution, called BERTRL (BERT-based Relational Learning), which
leverages pre-trained language model and fine-tunes it by taking relation
instances and their possible reasoning paths as training samples. BERTRL
outperforms the SOTAs in 15 out of 18 cases in both inductive and transductive
settings. Meanwhile, it demonstrates strong generalization capability in
few-shot learning and is explainable.
