.png){kind=icon}
.png){kind=icon}
Return
MultiTEmb: Multi-scale Embeddings for Temporal KG Completion
.png){kind=icon}
PRE
AI
DOI：10.1007/978-981-95-3061-8_17.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Temporal Knowledge Graph Completion is essential for many real-world applications, requiring effective modeling of temporal information. However, most existing methods rely on a single time scale, limiting their ability to capture dependencies over short and long time horizons. To address this, we propose MultiTEmb, a novel multi-scale temporal completion method. By decomposing temporal information into year, quarter, month, and day, we generate feature vectors at each scale and model dependencies using a Time-aware Efficient Self-Attention Mechanism (TE-SAM) with adaptive feature weighting. To further enhance feature fusion, we introduce InfoNCE-based contrastive learning to improve temporal representation discriminability and employ an Enhanced Gated Recurrent Unit (E-GRU) to sequentially integrate multi-scale embeddings. Extensive experiments on four benchmark datasets show that MultiTEmb significantly outperforms existing knowledge graph embedding and temporal knowledge graph completion models, demonstrating its effectiveness in temporal reasoning tasks.
Keywords:
Multi-Scale Temporal Features
Temporal Knowledge Graph Embedding
Contrastive Learning
Temporal Knowledge Graph
Journal
K
IF:
0
Papers:
29
Citations:
0