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.
Proceedings of the Sixteenth ACM International conference on web search and data mining , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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RCTEA introduces richness-guided co-training with attention and dual-view consensus to achieve state-of-the-art temporal entity alignment on public benchmarks.
citing papers explorer
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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.
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RCTEA: Richness-guided Co-training for Temporal Entity Alignment
RCTEA introduces richness-guided co-training with attention and dual-view consensus to achieve state-of-the-art temporal entity alignment on public benchmarks.