For knowledge graphs, knowledge graph embedding (KGE) models learn to project the symbolic entities and relations into a low-dimensional continuous vector space based on the observed triplets. However, existing KGE models can not 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  uses an iterative aggregation strategy to integrate the context information in multi-hop into the entity/relation embeddings , also explicitly models the graph context without introducing extra trainable parameters other than embeddings. Moreover , extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.
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.
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For knowledge graphs, knowledge
Knowledge
D
the
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low-dimensional
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can not
cannot
A
explicitly models the graph context without introducing redundant trainable parameters, and
D
in multi-hop
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, also explicitly models the graph context without introducing extra trainable parameters other than embeddings. Moreover
. As a generic framework, it can be used with many simple KGE models to achieve excellent results. Finally