【摘要】 链接预测一直以来是知识图谱补全领域的重要研究课题。如今仍然存在很多链接预测模型忽略了现实世界中实体之间的语义联系。针对有效利用实体间的语义信息提出圆柱坐标系中基于旋转嵌入的链接预测模型——RotatCY。将实体和关系嵌入到圆柱坐标系中，利用径向距离、高度和方位角结合的方式将实体按照语义层级划分，实现对实体间语义层级的建模。在五个标准数据集上进行实验，实验结果显示RotatCY模型的性能在四个评价指标上均有提升。与RotatH相比，RotatCY在FB15k数据集的Hits@1上提升最大，提高了0.143。更多还原

【Abstract】 Link prediction has always been an important research topic in the field of knowledge graph completion. Nowadays, there are still many link prediction models that ignore the semantic relationship between entities in the real world. In order to effectively use the semantic information between entities, a link prediction model based on rotation embedding in cylindrical coordinate system-RotatCY is proposed. The entities and relationships were embedded into the cylindrical coordinate system, and the entities were divided according to the semantic level by the combination of radial distance, height and angle, so as to achieve the modeling of the semantic level between entities. Experiments were conducted on five standard data sets. The experimental results show that the performance of the RotatCY model is improved in four evaluation indicators. Compared with RotatH, RotatCY has the largest improvement in Hits@1 of FB15k dataset, with an increase of 0.143.更多还原