Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a low-dimensional representation of a knowledge graph's entities and relations. This paper proposes "Farspredict" a Persian knowledge graph based on Farsbase (the most comprehensive knowledge graph in Persian). It also explains how the knowledge graph structure affects link prediction accuracy in KGE. To evaluate Farspredict, we implemented the popular models of KGE on it and compared the results with Freebase. Given the analysis results, some optimizations on the knowledge graph are carried out to improve its functionality in the KGE. As a result, a new Persian knowledge graph is achieved. Implementation results in the KGE models on Farspredict outperforming Freebases in many cases. At last, we discuss what improvements could be effective in enhancing the quality of Farspredict and how much it improves.

翻译：基于知识图谱嵌入（KGE）的链接预测是知识图谱补全的常用方法。此外，在非英语知识图谱上训练KGE模型，可促进这些语言环境下的知识抽取与知识推理。然而，非英语KGE在学习知识图谱实体和关系的低维表示时面临诸多挑战。本文提出基于Farsbase（波斯语最全面的知识图谱）构建的波斯语知识图谱"Farspredict"，并阐释知识图谱结构如何影响KGE中的链接预测精度。为评估Farspredict，我们在其上实现了KGE主流模型，并将结果与Freebase进行对比。基于分析结果，我们对知识图谱进行了若干优化以提升其在KGE中的性能，最终获得改进后的波斯语知识图谱。在Farspredict上实施KGE模型的结果显示，其性能在多数情况下优于Freebase。最后，我们探讨了哪些改进措施能有效提升Farspredict的质量，以及这些改进的具体提升幅度。