  Knowledge graph embedding (KGE) models learn to project symbolic entities and
relations into a continuous vector space based on the observed triplets.
However, existing KGE models cannot make a proper trade-off between the graph
context and the model complexity, which makes them still far from satisfactory.
In this paper, we propose a lightweight framework named LightCAKE for
context-aware KGE. LightCAKE explicitly models the graph context without
introducing redundant trainable parameters, and uses an iterative aggregation
strategy to integrate the context information into the entity/relation
embeddings. As a generic framework, it can be used with many simple KGE models
to achieve excellent results. Finally, extensive experiments on public
benchmarks demonstrate the efficiency and effectiveness of our framework.
