Knowledge graphs have shown increasing importance in broad applications such as question answering, web search, and recommendation systems. The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces to perform various machine learning tasks. Most of the existing works only focused on the local structure of knowledge graphs when utilizing structural information of entities, which may not sincerely preserve the global structure of knowledge graphs.In this paper, we propose a semi-supervised model by adopting graph convolutional networks to utilize both local and global structural information of entities. Specifically, our model takes textual information of each entity into consideration as entity attributes in the process of learning. We show the effectiveness of our model by applying it to two traditional tasks for knowledge graph: entity classification and link prediction. Experimental results on two well-known corpora reveal the advantages of this model compared to state-of-the-art methods on both tasks. Moreover, the results show that even with only 1% labeled data to train, our model can still achieve good performance.

中文翻译：

---

知识图嵌入的半监督模型

知识图在广泛的应用中显示出越来越高的重要性，例如问题解答，Web搜索和推荐系统。知识图嵌入的目的是将知识图的实体和关系编码为连续的低维向量空间，以执行各种机器学习任务。现有的大多数工作在利用实体的结构信息时只关注知识图的局部结构，可能并不能真正保留知识图的全局结构。本文采用图卷积网络提出了一种半监督模型。利用实体的本地和全局结构信息。具体而言，我们的模型在学习过程中将每个实体的文本信息作为实体属性考虑在内。通过将其应用于知识图的两个传统任务，我们展示了模型的有效性：实体分类和链接预测。与两个任务上的最新方法相比，在两个著名语料库上的实验结果表明了该模型的优势。而且，结果表明，即使只训练1％的标签数据，我们的模型仍可以实现良好的性能。