National Natural Science Foundation of China [61972290]; Hubei Provincial Natural Science Foundation of China [2023AFA096]; Self-determined research funds of CCNU from the colleges' basic research and operation of MOE [CCNU22QN015]; Wuhan Knowledge Innovation Project [2022010801010278]
机构署名：
本校为其他机构
院系归属：
计算机学院
摘要：
Temporal knowledge graph (TKG) reasoning aims to infer the missing links from the massive historical facts. One of the big issues is that how to model the entity evolution from both the local and especially global perspectives. The primary temporal dependency models often fail to disentangle both perspectives due to the lack explicit annotations to distinguish the boundary of these two representations. To address these limitations, we propose a contrastive learning framework to Disentangle Local and Global perspectives for TKG Reasoning with selfsupervision framework (DLGR). Our proposed DLGR ...
