TY - JOUR A2 - Guirao, Juan L. G. AU - Chen, Baiyang AU - Chen, Xiaoliang AU - Lu, Peng AU - Du, Yajun PY - 2020 DA - 2020/12/08 TI - CAREA: Cotraining Attribute and Relation Embeddings for Cross-Lingual Entity Alignment in Knowledge Graphs SP - 6831603 VL - 2020 AB - 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 ％ 在所有实验数据集中。代码可用 https://github.com/chenbaiyang/carea.。SN - 1026-0226 UR - https://doi.org/10.1155/2020/6831603 do - 10.1155 / 2020/6831603 jf - 自然和社会中的离散动态Pb - Hindawi Kw - ER -