  Recent works in neural knowledge graph inference attempt to combine logic
rules with knowledge graph embeddings to benefit from prior knowledge. However,
they usually cannot avoid rule grounding, and injecting a diverse set of rules
has still not been thoroughly explored. In this work, we propose InjEx, a
mechanism to inject multiple types of rules through simple constraints, which
capture definite Horn rules. To start, we theoretically prove that InjEx can
inject such rules. Next, to demonstrate that InjEx infuses interpretable prior
knowledge into the embedding space, we evaluate InjEx on both the knowledge
graph completion (KGC) and few-shot knowledge graph completion (FKGC) settings.
Our experimental results reveal that InjEx outperforms both baseline KGC models
as well as specialized few-shot models while maintaining its scalability and
efficiency.

Computation and Language;Artificial Intelligence;Machine Learning