Entity alignment is a viable means for integrating heterogeneous knowledge
among different knowledge graphs (KGs). Recent developments in the field often
take an embedding-based approach to model the structural information of KGs so
that entity alignment can be easily performed in the embedding space. However,
most existing works do not explicitly utilize useful relation representations
to assist in entity alignment, which, as we will show in the paper, is a simple
yet effective way for improving entity alignment. This paper presents a novel
joint learning framework for entity alignment. At the core of our approach is a
Graph Convolutional Network (GCN) based framework for learning both entity and
relation representations. Rather than relying on pre-aligned relation seeds to
learn relation representations, we first approximate them using entity
embeddings learned by the GCN. We then incorporate the relation approximation
into entities to iteratively learn better representations for both. Experiments
performed on three real-world cross-lingual datasets show that our approach
substantially outperforms state-of-the-art entity alignment methods.

本文提出了一种使用图卷积神经网络（GCN）学习实体和关系表示的新颖联合学习框架，以改善实体对齐，并证明利用有用的关系表示来协助实体对齐是一种简单而有效的方法。通过在三个真实的跨语言数据集上进行的实验表明，我们的方法显著优于现有的实体对齐方法。

联合学习实体和关系表示用于实体对齐

Jointly Learning Entity and Relation Representations for Entity  Alignment

We introduce a novel discriminative latent-variable model for the task of
bilingual lexicon induction. Our model combines the bipartite matching
dictionary prior of Haghighi et al. (2008) with a state-of-the-art
embedding-based approach. To train the model, we derive an efficient Viterbi EM
algorithm. We provide empirical improvements on six language pairs under two
metrics and show that the prior theoretically and empirically helps to mitigate
the hubness problem. We also demonstrate how previous work may be viewed as a
similarly fashioned latent-variable model, albeit with a different prior.

我们提出了一种新的判别式潜变量模型，用于双语词典归纳任务，该模型将 Haghighi 等人的二分匹配字典先验与最先进的基于嵌入的方法相结合。我们提供了在两个度量标准下六个语言对上的经验改进，并表明先前的工作可以被视为类似配置的潜变量模型，尽管具有不同的先验。