Knowledge bases (KBs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform knowledge base completion or link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This article serves as a brief overview of embedding models of entities and relationships for knowledge base completion, summarizing up-to-date experimental results on standard benchmark datasets FB15k, WN18, FB15k-237, WN18RR, FB13 and WN11.

翻译：关于实体及其关系真实世界事实的知识基础(KBs)是各种自然语言处理任务的有用资源,然而,由于知识基础通常不完整,因此,能够完成知识基础或进行联系预测是有益的,即预测知识库中不存在的关系是否可能是真实的。本篇文章简要概述了实体嵌入模型和知识基础完成关系,总结了标准基准数据集FB15k、WN18、FB15k-237、WN18RR、FB13和WN11的最新实验结果。