RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling
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A differentiable neural architecture learns lifted action schemas and identifies unobserved action arguments from state-change traces in planning domains.
KGEMs for link prediction exhibit high instability in predictions and embeddings from initialization, negative sampling, and other factors, with better MRR not ensuring higher stability.
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.
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
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Differentiable Learning of Lifted Action Schemas for Classical Planning
A differentiable neural architecture learns lifted action schemas and identifies unobserved action arguments from state-change traces in planning domains.