English(EN) Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

GNNs 创建层级感知的知识图谱嵌入用于酵母表型预测

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-05 12:34

研究人员开发了一种新颖的方法，使用图神经网络（GNNs）为知识图谱创建层级感知的嵌入。该方法结合了源自本体的语义损失，以更好地表示领域知识。该方法应用于预测酵母表型，在双基因敲除实验中取得了 0.360 的平均 R^2 分数，优于基线模型。结合语义损失进一步将预测性能提高到 R^2=0.377，证明了本体结构在定量预测中的价值，并可能指导生物学发现。 AI

影响通过改进知识图谱和本体的定量预测来增强生物学发现。

排序理由学术论文，详细介绍了一种新的知识图谱嵌入方法及其应用。

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Filip Kronstr\"om, Alexander H. Gower, Daniel Brunns{\aa}ker, Ievgeniia A. Tiukova, Ross D. King · 2026-05-06 04:00

Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

arXiv:2605.03690v1 Announce Type: new Abstract: We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss derived from underlying ontologies. This method yields embeddings that better refl…
arXiv cs.AI TIER_1 English(EN) · Ross D. King · 2026-05-05 12:34

Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss derived from underlying ontologies. This method yields embeddings that better reflect domain knowledge. To demonstrate their utili…

报道来源 [2]

Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

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