SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction

Min Peng

Abstract:

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-theart baselines. The implementation of SMiLE is
available at https://github.com/GKNL/SMiLE.