  Knowledge Graph Embeddings (KGE) aim to map entities and relations to low
dimensional spaces and have become the \textit{de-facto} standard for knowledge
graph completion. Most existing KGE methods suffer from the sparsity challenge,
where it is harder to predict entities that appear less frequently in knowledge
graphs. In this work, we propose a novel framework KRACL to alleviate the
widespread sparsity in KGs with graph context and contrastive learning.
Firstly, we propose the Knowledge Relational Attention Network (KRAT) to
leverage the graph context by simultaneously projecting neighboring triples to
different latent spaces and jointly aggregating messages with the attention
mechanism. KRAT is capable of capturing the subtle semantic information and
importance of different context triples as well as leveraging multi-hop
information in knowledge graphs. Secondly, we propose the knowledge contrastive
loss by combining the contrastive loss with cross entropy loss, which
introduces more negative samples and thus enriches the feedback to sparse
entities. Our experiments demonstrate that KRACL achieves superior results
across various standard knowledge graph benchmarks, especially on WN18RR and
NELL-995 which have large numbers of low in-degree entities. Extensive
experiments also bear out KRACL's effectiveness in handling sparse knowledge
graphs and robustness against noisy triples.
