Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.

中文翻译：

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基于百科全书的藏文知识图谱表示学习联合模型

学习知识图谱的表示对于自然语言处理领域至关重要。英语知识图谱表示有很多研究。然而，对于藏语等资源匮乏的语言，如何表示稀疏的知识图谱是一个关键问题。本文针对藏文知识图谱的稀缺性，利用高资源语言知识图谱和Point of Information地图信息的三元组对藏文知识图谱进行扩展。为了改进藏文知识图谱的表示学习，我们提出了一种基于翻译嵌入和卷积神经网络模型的联合模型来合并结构和实体描述信息。此外，为了解决分割错误，我们使用字符和词嵌入来学习更复杂的藏语信息。最后，实验结果表明，我们的模型可以比基线更好地表示藏文知识图谱。