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

2022-02-10 08:40:09
Baoxin Wang, Qingye Meng, Ziyue Wang, Dayong Wu, Wanxiang Che, Shijin Wang, Zhigang Chen, Cong Liu
arXiv_CL
               
Abstract

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 a novel distance-based method named InterHT that allows 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.

Abstract (translated)

URL

https://arxiv.org/abs/2202.04897

PDF

https://arxiv.org/pdf/2202.04897.pdf

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