  Inductive link prediction -- where entities during training and inference
stages can be different -- has been shown to be promising for completing
continuously evolving knowledge graphs. Existing models of inductive reasoning
mainly focus on predicting missing links by learning logical rules. However,
many existing approaches do not take into account semantic correlations between
relations, which are commonly seen in real-world knowledge graphs. To address
this challenge, we propose a novel inductive reasoning approach, namely TACT,
which can effectively exploit Topology-Aware CorrelaTions between relations in
an entity-independent manner. TACT is inspired by the observation that the
semantic correlation between two relations is highly correlated to their
topological structure in knowledge graphs. Specifically, we categorize all
relation pairs into several topological patterns, and then propose a Relational
Correlation Network (RCN) to learn the importance of the different patterns for
inductive link prediction. Experiments demonstrate that TACT can effectively
model semantic correlations between relations, and significantly outperforms
existing state-of-the-art methods on benchmark datasets for the inductive link
prediction task.
