论文标题
连接知识图实体键入的嵌入
Connecting Embeddings for Knowledge Graph Entity Typing
论文作者
论文摘要
知识图（kg）实体键入旨在推断kg中可能的缺失实体类型实例，这是一个非常重要但仍未探索的知识图完成子任务。在本文中，我们提出了一种针对KG实体打字的新方法，该方法通过共同利用现有实体类型主张和来自KG的全球三重知识的本地键入知识进行培训。具体而言，我们提出了实体类型推理的两个不同知识驱动的有效机制。因此，我们构建了两个新颖的嵌入模型以实现机制。之后，使用与它们的联合模型推断缺失的实体类型实例，这有利于推论与实体类型实例和KG中的三重知识一致。两个现实世界数据集（FreeBase和Yago）的实验结果证明了我们提出的机制和模型改善KG实体键入的有效性。可以从：https：//github.com/ adam1679/connecte获得本文的源代码和数据
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: https://github.com/ Adam1679/ConnectE
