Knowledge graphs (KGs) are one of the most widely used techniques of knowledge organizations and have been extensively used in many application fields related to artificial intelligence, for example, web search and recommendations. Entity alignment provides a useful tool for how to integrate multilingual KGs automatically. However, most of the existing studies evaluated ignore the abundant information of entity attributes except for entity relationships. This paper sets out to investigate cross-lingual entity alignment and proposes an iterative cotraining approach (CAREA) to train a pair of independent models. The two models can extract the attribute and the relation features of multilingual KGs, respectively. In each iteration, the two models alternate to predict a new set of potentially aligned entity pairs. Besides, this method further filters through the dynamic threshold value to enhance the two models’ supervision. Experimental results on three real-world datasets demonstrate the effectiveness and superiority of the proposed method. The CAREA model improves the performance with at least an absolute increase of 3.9 across all experiment datasets. The code is available at https://github.com/ChenBaiyang/CAREA.

中文翻译：

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CAREA：知识图中跨语言实体对齐的协同训练属性和关系嵌入

知识图谱（KGs）是知识组织中使用最广泛的技术之一，并已广泛用于与人工智能有关的许多应用领域，例如Web搜索和推荐。实体对齐为如何自动集成多语言KG提供了有用的工具。但是，大多数现有研究评估都忽略了除实体关系之外的大量实体属性信息。本文着手研究跨语言的实体对齐方式，并提出了一种迭代式协同训练方法（CAREA）来训练一对独立的模型。这两个模型可以分别提取多语言KG的属性和相关特征。在每次迭代中，两个模型交替预测一组新的潜在对齐的实体对。除了，该方法进一步过滤动态阈值，以增强两个模型的监管。在三个真实数据集上的实验结果证明了该方法的有效性和优越性。CAREA模型至少绝对提高了3.9，从而提高了性能所有实验数据集。该代码位于https://github.com/ChenBaiyang/CAREA。