DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph

📰 ArXiv cs.AI

DyMRL is a dynamic multispace representation learning approach for multimodal event forecasting in knowledge graphs

advanced Published 27 Mar 2026

Action Steps

Learn time-sensitive information of different modalities
Fusion of multimodal knowledge using dynamic structural modality
Apply DyMRL to knowledge graphs for event forecasting

Who Needs to Know This

AI engineers and researchers working on multimodal event forecasting can benefit from DyMRL, as it enables the dynamic acquisition and fusion of multimodal knowledge

Key Insight

💡 DyMRL enables accurate representation of multimodal knowledge for event forecasting in dynamic settings

Key Takeaways

DyMRL is a dynamic multispace representation learning approach for multimodal event forecasting in knowledge graphs

Full Article

Title: DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph

Abstract:
arXiv:2603.24636v1 Announce Type: cross Abstract: Accurate representation of multimodal knowledge is crucial for event forecasting in real-world scenarios. However, existing studies have largely focused on static settings, overlooking the dynamic acquisition and fusion of multimodal knowledge. 1) At the knowledge acquisition level, how to learn time-sensitive information of different modalities, especially the dynamic structural modality. Existing dynamic learning methods are often limited to sh

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