  Link prediction is the task of inferring missing links between entities in
knowledge graphs. Embedding-based methods have shown effectiveness in
addressing this problem by modeling relational patterns in triples. However,
the link prediction task often requires contextual information in entity
neighborhoods, while most existing embedding-based methods fail to capture it.
Additionally, little attention is paid to the diversity of entity
representations in different contexts, which often leads to false prediction
results. In this situation, we consider that the schema of knowledge graph
contains the specific contextual information, and it is beneficial for
preserving the consistency of entities across contexts. In this paper, we
propose a novel Schema-augmented Multi-level contrastive LEarning framework
(SMiLE) to conduct knowledge graph link prediction. Specifically, we first
exploit network schema as the prior constraint to sample negatives and
pre-train our model by employing a multi-level contrastive learning method to
yield both prior schema and contextual information. Then we fine-tune our model
under the supervision of individual triples to learn subtler representations
for link prediction. Extensive experimental results on four knowledge graph
datasets with thorough analysis of each component demonstrate the effectiveness
of our proposed framework against state-of-the-art baselines. The
implementation of SMiLE is available at https://github.com/GKNL/SMiLE.
