Paper ID: 2202.04897

InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities

Baoxin Wang, Qingye Meng, Ziyue Wang, Honghong Zhao, Dayong Wu, Wanxiang Che, Shijin Wang, Zhigang Chen, Cong Liu

Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction task, which predicts the result by the distance between two entity representations. However, most of these methods represent the head entity and tail entity separately, which limits the model capacity. We propose two novel distance-based methods named InterHT and InterHT+ that allow the head and tail entities to interact better and get better entity representation. Experimental results show that our proposed method achieves the best results on ogbl-wikikg2 dataset.

Submitted: Feb 10, 2022