GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction , volume =
2 Pith papers cite this work, alongside 396 external citations. Polarity classification is still indexing.
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Polaris separates semantic meaning from hierarchical structure in embeddings via angular geometry and radius on a hypersphere, yielding up to 19-point gains in taxonomy expansion retrieval over baselines.
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Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
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Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Polaris separates semantic meaning from hierarchical structure in embeddings via angular geometry and radius on a hypersphere, yielding up to 19-point gains in taxonomy expansion retrieval over baselines.