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.

通过将类型聚类信息有效地融入实体和类型的表示中，Cross-view Optimal Transport（COTET）方法通过多视图生成和编码、交叉视图最优传输和基于汇聚机制的实体类型预测模块，在知识图谱实体类型推理任务中展现出了显著的效果。