With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these models using an increasingly complex feature space, we show that simple changes in the architecture of the underlying model can outperform state-of-the-art models without the need for complex feature engineering. In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph's entities and edges, and through subtle, but important, changes to the standard loss function. In doing so, ProjE has a parameter size that is smaller than 11 out of 15 existing methods while performing $37\%$ better than the current-best method on standard datasets. We also show, via a new fact checking task, that ProjE is capable of accurately determining the veracity of many declarative statements.

本文介绍了 ProjE, 通过学习知识图谱中的实体和边缘的联合嵌入，以及对标准损失函数进行微妙但重要的更改，ProjE 填补了知识图谱中的缺失信息。结果表明，ProjE 使用一个参数大小比现有的15种方法中的11个方法要小，而且表现比标准数据集上的现有最佳方法好37％。我们还发现 ProjE 能够通过一项新的事实检查任务准确地确定许多陈述性语句的真实性。

ProjE：用于知识图谱完成的嵌入投影