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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The thesis proposes specialized algebraic, logical, and geometric methods to enable scalable reasoning over imprecise attributes, probabilistic triples, and incomplete schemas in knowledge graphs.
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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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Scalable Uncertainty Reasoning in Knowledge Graphs
The thesis proposes specialized algebraic, logical, and geometric methods to enable scalable reasoning over imprecise attributes, probabilistic triples, and incomplete schemas in knowledge graphs.