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The dominant paradigm for relation prediction in knowledge graphs involveslearning and operating on latent representations (i.e., embeddings) of entitiesand relations. However, these embedding-based methods do not explicitly capturethe compositional logical rules underlying the knowledge graph, and they arelimited to the transductive setting, where the full set of entities must beknown during training. Here, we propose a graph neural network based relationprediction framework, GraIL, that reasons over local subgraph structures andhas a strong inductive bias to learn entity-independent relational semantics.Unlike embedding-based models, GraIL is naturally inductive and can generalizeto unseen entities and graphs after training. We provide theoretical proof andstrong empirical evidence that GraIL can represent a useful subset offirst-order logic and show that GraIL outperforms existing rule-inductionbaselines in the inductive setting. We also demonstrate significant gainsobtained by ensembling GraIL with various knowledge graph embedding methods inthe transductive setting, highlighting the complementary inductive bias of ourmethod.

Inductive Relation Prediction by Subgraph Reasoning