Beyond Entity Variance: Deep Dive into Uncertainty in Knowledge Graph Embeddings
Published:Dec 26, 2025 12:38
•1 min read
•ArXiv
Analysis
This ArXiv paper addresses a crucial aspect of knowledge graph embeddings by moving beyond simple variance measures of entities. The research likely offers valuable insights into more robust and nuanced uncertainty modeling for knowledge graph representation and inference.
Key Takeaways
- •Explores uncertainty modeling in the context of knowledge graph embeddings.
- •Suggests limitations of using only entity variance as a measure of uncertainty.
- •Likely proposes new methods for representing and reasoning with uncertain knowledge.
Reference
“The research focuses on decomposing uncertainty in probabilistic knowledge graph embeddings.”