  Knowledge graph embedding (KGE) aims at learning powerful representations to
benefit various artificial intelligence applications. Meanwhile, contrastive
learning has been widely leveraged in graph learning as an effective mechanism
to enhance the discriminative capacity of the learned representations. However,
the complex structures of KG make it hard to construct appropriate contrastive
pairs. Only a few attempts have integrated contrastive learning strategies with
KGE. But, most of them rely on language models ( e.g., Bert) for contrastive
pair construction instead of fully mining information underlying the graph
structure, hindering expressive ability. Surprisingly, we find that the
entities within a relational symmetrical structure are usually similar and
correlated. To this end, we propose a knowledge graph contrastive learning
framework based on relation-symmetrical structure, KGE-SymCL, which mines
symmetrical structure information in KGs to enhance the discriminative ability
of KGE models. Concretely, a plug-and-play approach is proposed by taking
entities in the relation-symmetrical positions as positive pairs. Besides, a
self-supervised alignment loss is designed to pull together positive pairs.
Experimental results on link prediction and entity classification datasets
demonstrate that our KGE-SymCL can be easily adopted to various KGE models for
performance improvements. Moreover, extensive experiments show that our model
could outperform other state-of-the-art baselines.
