EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
arXiv preprint arXiv:2309.01538 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
verdicts
UNVERDICTED 2representative citing papers
LLM-assisted pipeline jointly generates logical formulas and executable predicates for rule-based verification of HD map transformations in CommonRoad, evaluated on synthetic bridge and slope scenarios.
citing papers explorer
-
EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
-
LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations
LLM-assisted pipeline jointly generates logical formulas and executable predicates for rule-based verification of HD map transformations in CommonRoad, evaluated on synthetic bridge and slope scenarios.