.png){kind=icon}
.png){kind=icon}
Return
Dual-view temporal knowledge graph reasoning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2025.114330.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Temporal Knowledge Graph (TKG) reasoning has attracted significant attention for completing missing knowledge over time. Recent graph neural network (GNN) -based approaches that explore the temporal evolution of graph topological structures from either continuous-time or discrete-time, which offer distinct perspectives on modeling event associations in TKG. Two GNN-based approaches with different perspectives are supposed to be complementary, but effective integration has not been thoroughly explored in existing research. In addition, capturing the repetitive nature of events during GNN message passing poses a challenge in the continuous-time view, while the complex associations among co-occurring events in KG snapshots cannot be efficiently modeled in the discrete-time view. In this paper, we propose a new Dual-view TKG reasoning network, namely DV-TKR, which comprehensively models the temporal semantic information by integrating the strengths of both types of graph structure encoding representation for reasoning. In DV-TKR, we decompose the quadruple neighbors of each entity into triples and times in the continuous-time TKG. A time-aware event recurring modeling (TERM) module incorporating multiple attention mechanisms in the continuous-time view, is proposed to effectively distinguish the importance of the same triple at different times. For the discrete-time view, we propose a relation-aware graph evolving modeling (RGEM) module to learn the temporal evolution of entities among successive KG snapshots. The relation-aware graph attention mechanism in the RGEM module captures significant correlations among co-occurring events within the overall KG snapshot. Extensive experimental results on three public datasets demonstrate the superiority of our proposed model compared to the state-of-the-art baselines.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W