  The dominant paradigm for relation prediction in knowledge graphs involves
learning and operating on latent representations (i.e., embeddings) of entities
and relations. However, these embedding-based methods do not explicitly capture
the compositional logical rules underlying the knowledge graph, and they are
limited to the transductive setting, where the full set of entities must be
known during training. Here, we propose a graph neural network based relation
prediction framework, GraIL, that reasons over local subgraph structures and
has a strong inductive bias to learn entity-independent relational semantics.
Unlike embedding-based models, GraIL is naturally inductive and can generalize
to unseen entities and graphs after training. We provide theoretical proof and
strong empirical evidence that GraIL can represent a useful subset of
first-order logic and show that GraIL outperforms existing rule-induction
baselines in the inductive setting. We also demonstrate significant gains
obtained by ensembling GraIL with various knowledge graph embedding methods in
the transductive setting, highlighting the complementary inductive bias of our
method.
