Papers by Ruijiang Li

Copied to clipboard

Challenge:	Existing models for link prediction fail to capture relation compositions, resulting in lack of interpretability for reasoning on incomplete knowledge graph (KG).
Approach:	They propose a new model that learns knowledge graph embeddings that can capture relation compositions by nature and reduces the solution space drastically.
Outcome:	The proposed model outperforms existing models and is comparable to or better than deep learning models such as ConvE.