A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion

Abstract

Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-art performance. In order to understand the current research status of TKGC methods based on deep learning and promote further development in this field, in this paper, for the first time, we summarize the deep learning-based methods in TKGC research. First, we detail the background of TKGC, including task definition, benchmark datasets, and evaluation protocol. Then, we divide the existing deep learning-based TKGC methods into eight fine-grained categories according to their core technology and summarize them. Finally, we conclude the paper and present three future research directions for TKGC.

Keywords

• knowledge graph
• temporal knowledge graph completion
• deep learning