A contrastive learning framework with dual gates and noise awareness for temporal knowledge graph reasoning

Abstract Temporal knowledge graph reasoning(TKGR) has attracted widespread attention due to its ability to handle dynamic temporal features. However, existing methods face three major challenges: (1) the difficulty of capturing long-distance dependencies in information sparse environments; (2) the p...

Descripción completa

Detalles Bibliográficos

Autores principales:	Siling Feng, Bolin Chen, Qian Liu, Mengxing Huang
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Nature Portfolio 2025-05-01
Colección:	Scientific Reports
Materias:	Temporal knowledge graph reasoning Dual-gate selection Long-distance dependencies Noise-aware adversarial modeling Multi-layer embedding contrastive learning Robustness
Acceso en línea:	https://doi.org/10.1038/s41598-025-00314-w
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!