시간 지식 그래프에서 진화적 사건 체인을 학습하기 위한 Transformer 기반 추론 모델 ECEformer를 제안한다.
The core message of this paper is to propose an innovative Transformer-based network, dubbed ECEformer, that learns the evolutionary chain of events (ECE) to enhance the performance of temporal knowledge graph reasoning (TKGR).
The core message of this paper is to propose a model called Repeating-Local-Global History Network (RLGNet) that effectively integrates repeating, local, and global historical information to improve the accuracy of temporal knowledge graph reasoning.