A Convolutional Neural Network With Time-Aware Channel Weighting for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) extend traditional knowledge graphs by incorporating a temporal dimension into triples, enabling a more precise modeling of dynamic relationships. However, TKGs often face challenges, such as data sparsity and incomplete information in real-world applications, which...
Saved in:
| Main Authors: | Kesheng Zhang, Guige Ouyang, Yongzhong Huang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11008654/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Learning temporal granularity with quadruplet networks for temporal knowledge graph completion
by: Rushan Geng, et al.
Published: (2025-05-01) -
A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
by: Ningning Jia, et al.
Published: (2024-10-01) -
Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion
by: Fan Li, et al.
Published: (2025-06-01) -
Multi-channel based edge-learning graph convolutional network
by: Shuai YANG, et al.
Published: (2022-09-01) -
Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting
by: Na Hu, et al.
Published: (2022-12-01)