Representation Learning of Knowledge Graphs with Hierarchical Types

Experimental results show that the proposed Type-embodied Knowledge Representation Learning models significantly outperform all baselines on both tasks, especially with long-tail distribution, and indicates that the models are capable of capturing hierarchical type information which is significant when constructing representations of knowledge graphs.