  Due to the open world assumption, Knowledge Graphs (KGs) are never complete.
In order to address this issue, various Link Prediction (LP) methods are
proposed so far. Some of these methods are inductive LP models which are
capable of learning representations for entities not seen during training.
However, to the best of our knowledge, none of the existing inductive LP models
focus on learning representations for unseen relations. In this work, a novel
Relation Aware Inductive Link preDiction (RAILD) is proposed for KG completion
which learns representations for both unseen entities and unseen relations. In
addition to leveraging textual literals associated with both entities and
relations by employing language models, RAILD also introduces a novel
graph-based approach to generate features for relations. Experiments are
conducted with different existing and newly created challenging benchmark
datasets and the results indicate that RAILD leads to performance improvement
over the state-of-the-art models. Moreover, since there are no existing
inductive LP models which learn representations for unseen relations, we have
created our own baselines and the results obtained with RAILD also outperform
these baselines.
