A dual-purpose benchmark supplies two text-derived knowledge graphs and one expert reference graph on the same biomedical corpus to jointly measure construction method quality and GNN robustness via semi-supervised node classification.
Nucleic acids research , volume=
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LogosKG delivers a novel hardware-aligned system for efficient multi-hop retrieval on billion-edge knowledge graphs without sacrificing fidelity, demonstrated via biomedical KG-LLM applications.
Reweighting the training loss to emphasize semantically salient tokens lets ophthalmological report generation models reach similar quality with up to ten times less data.
citing papers explorer
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A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
A dual-purpose benchmark supplies two text-derived knowledge graphs and one expert reference graph on the same biomedical corpus to jointly measure construction method quality and GNN robustness via semi-supervised node classification.
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LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval
LogosKG delivers a novel hardware-aligned system for efficient multi-hop retrieval on billion-edge knowledge graphs without sacrificing fidelity, demonstrated via biomedical KG-LLM applications.
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Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
Reweighting the training loss to emphasize semantically salient tokens lets ophthalmological report generation models reach similar quality with up to ten times less data.