OKH-RAG represents knowledge as ordered hyperedges and retrieves coherent interaction sequences via a learned transition model, outperforming permutation-invariant RAG baselines on order-sensitive QA tasks.
Knowledge hypergraphs: Prediction beyond binary relations
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A comprehensive survey of graph-based frameworks for higher-order networks, covering foundational concepts, extensions, and newly introduced formalisms with emphasis on structural principles and applications.
citing papers explorer
-
Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models
OKH-RAG represents knowledge as ordered hyperedges and retrieves coherent interaction sequences via a learned transition model, outperforming permutation-invariant RAG baselines on order-sensitive QA tasks.
-
Representing Higher-Order Networks: A Survey of Graph-Based Frameworks
A comprehensive survey of graph-based frameworks for higher-order networks, covering foundational concepts, extensions, and newly introduced formalisms with emphasis on structural principles and applications.