Knowledge graph (KG) entity typing aims at inferring possible missing entity
type instances in KG, which is a very significant but still under-explored
subtask of knowledge graph completion. In this paper, we propose a novel
approach for KG entity typing which is trained by jointly utilizing local
typing knowledge from existing entity type assertions and global triple
knowledge from KGs. Specifically, we present two distinct knowledge-driven
effective mechanisms of entity type inference. Accordingly, we build two novel
embedding models to realize the mechanisms. Afterward, a joint model with them
is used to infer missing entity type instances, which favors inferences that
agree with both entity type instances and triple knowledge in KGs. Experimental
results on two real-world datasets (Freebase and YAGO) demonstrate the
effectiveness of our proposed mechanisms and models for improving KG entity
typing. The source code and data of this paper can be obtained from:
this https URL Adam1679/ConnectE

本文提出了一种新方法，通过共同利用现有实体类型和知识图谱中的全局三元组知识以及两个嵌入模型，推断知识图谱中缺失的实体类型实例；在真实数据集上实验结果显示，此方法和模型可以有效地提高知识图谱实体分类。