Knowledge graph embedding aims to represent entities and relations in a
large-scale knowledge graph as elements in a continuous vector space. Existing
methods, e.g., TransE and TransH, learn embedding representation by defining a
global margin-based loss function over the data. However, the optimal loss
function is determined during experiments whose parameters are examined among a
closed set of candidates. Moreover, embeddings over two knowledge graphs with
different entities and relations share the same set of candidate loss
functions, ignoring the locality of both graphs. This leads to the limited
performance of embedding related applications. In this paper, we propose a
locally adaptive translation method for knowledge graph embedding, called
TransA, to find the optimal loss function by adaptively determining its margin
over different knowledge graphs. Experiments on two benchmark data sets
demonstrate the superiority of the proposed method, as compared to
the-state-of-the-art ones.

该论文提出了一种名为 TransA 的知识图谱嵌入方法，通过局部自适应方法确定不同知识图谱上的最优损失函数来提高嵌入相关应用的性能，针对两个基准数据集的实验结果表明该方法具有超过现有方法的优越性。

面向知识图谱嵌入的本地自适应翻译

Locally Adaptive Translation for Knowledge Graph Embedding

Knowledge representation is a major topic in AI, and many studies attempt to
represent entities and relations of knowledge base in a continuous vector
space. Among these attempts, translation-based methods build entity and
relation vectors by minimizing the translation loss from a head entity to a
tail one. In spite of the success of these methods, translation-based methods
also suffer from the oversimplified loss metric, and are not competitive enough
to model various and complex entities/relations in knowledge bases. To address
this issue, we propose \textbf{TransA}, an adaptive metric approach for
embedding, utilizing the metric learning ideas to provide a more flexible
embedding method. Experiments are conducted on the benchmark datasets and our
proposed method makes significant and consistent improvements over the
state-of-the-art baselines.

研究了知识表示在人工智能中的重要性，提出了基于转换的方法矢量化实体和关系，然后通过使用基于度量学习的自适应度量方法来提高性能。在基准数据集上通过实验证明了该方法的显着改进，领先于现有的基线模型。