In this paper we present a unified framework for modeling multi-relational
representations, scoring, and learning, and conduct an empirical study of
several recent multi-relational embedding models under the framework. We
investigate the different choices of relation operators based on linear and
bilinear transformations, and also the effects of entity representations by
incorporating unsupervised vectors pre-trained on extra textual resources. Our
results show several interesting findings, enabling the design of a simple
embedding model that achieves the new state-of-the-art performance on a popular
knowledge base completion task evaluated on Freebase.

本文提出了一种统一的模型框架，用于建模多关系表示、评分和学习，并在此框架下对几种最近的多关系嵌入模型进行了实证研究。研究了基于线性和双线性变换的关系算子的不同选择，以及通过整合额外文本资源上的非监督向量进行实体表示的效果。结果表明了几个有趣的发现，实现了在 Freebase 上进行评估的热门知识库完成任务的最新性能的简单嵌入模型的设计。