Knowledge graphs are useful for many artificial intelligence (AI) tasks.
However, knowledge graphs often have missing facts. To populate the graphs,
knowledge graph embedding models have been developed. Knowledge graph embedding
models map entities and relations in a knowledge graph to a vector space and
predict unknown triples by scoring candidate triples. TransE is the first
translation-based method and it is well known because of its simplicity and
efficiency for knowledge graph completion. It employs the principle that the
differences between entity embeddings represent their relations. The principle
seems very simple, but it can effectively capture the rules of a knowledge
graph. However, TransE has a problem with its regularization. TransE forces
entity embeddings to be on a sphere in the embedding vector space. This
regularization warps the embeddings and makes it difficult for them to fulfill
the abovementioned principle. The regularization also affects adversely the
accuracies of the link predictions. On the other hand, regularization is
important because entity embeddings diverge by negative sampling without it.
This paper proposes a novel embedding model, TorusE, to solve the
regularization problem. The principle of TransE can be defined on any Lie
group. A torus, which is one of the compact Lie groups, can be chosen for the
embedding space to avoid regularization. To the best of our knowledge, TorusE
is the first model that embeds objects on other than a real or complex vector
space, and this paper is the first to formally discuss the problem of
regularization of TransE. Our approach outperforms other state-of-the-art
approaches such as TransE, DistMult and ComplEx on a standard link prediction
task. We show that TorusE is scalable to large-size knowledge graphs and is
faster than the original TransE.

本文提出一种新的知识图谱嵌入模型 ToruE，解决了传统模型 TransE 中由于规范化导致的问题，并在链接预测任务上表现优异。

TorusE: 嵌入到李群上的知识图谱嵌入

TorusE: Knowledge Graph Embedding on a Lie Group

Knowledge bases are useful resources for many natural language processing
tasks, however, they are far from complete. In this paper, we define a novel
entity representation as a mixture of its neighborhood in the knowledge base
and apply this technique on TransE-a well-known embedding model for knowledge
base completion. Experimental results show that the neighborhood information
significantly helps to improve the results of the TransE model, leading to
better performance than obtained by other state-of-the-art embedding models on
three benchmark datasets for triple classification, entity prediction and
relation prediction tasks.

本文介绍了一种新的实体表示方法，并将其应用于知识库补全中的 TransE 嵌入模型，实验证明这种方法可以明显提高 TransE 模型在三种基准数据集上的性能。

邻域混合模型用于知识库补全

Neighborhood Mixture Model for Knowledge Base Completion

Knowledge graph embedding refers to projecting entities and relations in
knowledge graph into continuous vector spaces. State-of-the-art methods, such
as TransE, TransH, and TransR build embeddings by treating relation as
translation from head entity to tail entity. However, previous models can not
deal with reflexive/one-to-many/many-to-one/many-to-many relations properly, or
lack of scalability and efficiency. Thus, we propose a novel method, flexible
translation, named TransF, to address the above issues. TransF regards relation
as translation between head entity vector and tail entity vector with flexible
magnitude. To evaluate the proposed model, we conduct link prediction and
triple classification on benchmark datasets. Experimental results show that our
method remarkably improve the performance compared with several
state-of-the-art baselines.

本文介绍了知识图谱嵌入的相关研究，提出了一种新的模型 ——TransF，有效地解决了前置模型无法处理的关系问题，并通过基准数据集的链接预测和三元组分类实验，证明了该模型相对于其他前沿模型具有明显的性能提升。