A semi-supervised model for knowledge graph embedding

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.