  Knowledge graph entity typing (KGET) aims at inferring plausible types of
entities in knowledge graphs. Existing approaches to KGET focus on how to
better encode the knowledge provided by the neighbors and types of an entity
into its representation. However, they ignore the semantic knowledge provided
by the way in which types can be clustered together. In this paper, we propose
a novel method called Multi-view Contrastive Learning for knowledge graph
Entity Typing (MCLET), which effectively encodes the coarse-grained knowledge
provided by clusters into entity and type embeddings. MCLET is composed of
three modules: i) Multi-view Generation and Encoder module, which encodes
structured information from entity-type, entity-cluster and cluster-type views;
ii) Cross-view Contrastive Learning module, which encourages different views to
collaboratively improve view-specific representations of entities and types;
iii) Entity Typing Prediction module, which integrates multi-head attention and
a Mixture-of-Experts strategy to infer missing entity types. Extensive
experiments show the strong performance of MCLET compared to the
state-of-the-art
