  Knowledge graph entity typing (KGET) aims to infer missing entity type
instances in knowledge graphs. Previous research has predominantly centered
around leveraging contextual information associated with entities, which
provides valuable clues for inference. However, they have long ignored the dual
nature of information inherent in entities, encompassing both high-level
coarse-grained cluster knowledge and fine-grained type knowledge. This paper
introduces Cross-view Optimal Transport for knowledge graph Entity Typing
(COTET), a method that effectively incorporates the information on how types
are clustered into the representation of entities and types. COTET comprises
three modules: i) Multi-view Generation and Encoder, which captures structured
knowledge at different levels of granularity through entity-type,
entity-cluster, and type-cluster-type perspectives; ii) Cross-view Optimal
Transport, transporting view-specific embeddings to a unified space by
minimizing the Wasserstein distance from a distributional alignment
perspective; iii) Pooling-based Entity Typing Prediction, employing a mixture
pooling mechanism to aggregate prediction scores from diverse neighbors of an
entity. Additionally, we introduce a distribution-based loss function to
mitigate the occurrence of false negatives during training. Extensive
experiments demonstrate the effectiveness of COTET when compared to existing
baselines.
