Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation
#Temporal Knowledge Graph #Diffusion Models #TKG Extrapolation #Negative-Aware Diffusion #Generative AI #arXiv #Predictive Analytics
📌 Key Takeaways
- A new paper on arXiv introduces a Negative-Aware Diffusion Process for temporal reasoning.
- The model addresses the lack of negative context in traditional generative diffusion paths.
- Researchers argue that existing cross-entropy ranking methods limit the accuracy of candidate distributions.
- The innovation aims to improve how AI predicts future facts using historical knowledge graphs.
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🏷️ Themes
Artificial Intelligence, Data Science, Machine Learning
📚 Related People & Topics
Predictive analytics
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Generative artificial intelligence
Subset of AI using generative models
# Generative Artificial Intelligence (GenAI) **Generative artificial intelligence** (also referred to as **generative AI** or **GenAI**) is a specialized subfield of artificial intelligence focused on the creation of original content. Utilizing advanced generative models, these systems are capable ...
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📄 Original Source Content
arXiv:2602.08815v1 Announce Type: new Abstract: Temporal Knowledge Graph (TKG) reasoning seeks to predict future missing facts from historical evidence. While diffusion models (DM) have recently gained attention for their ability to capture complex predictive distributions, two gaps remain: (i) the generative path is conditioned only on positive evidence, overlooking informative negative context, and (ii) training objectives are dominated by cross-entropy ranking, which improves candidate order