Relation operators in knowledge graphs are Kraus channels obeying three axioms, enabling KrausKGE which outperforms baselines on N-to-N relations and supplies a rank-based complexity measure.
Convolutional 2d knowledge graph embeddings
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RALP learns string-based chain-of-thought prompts as scoring functions for knowledge graph triples using Bayesian optimization from fewer than 30 examples, improving link prediction MRR by over 5% and achieving over 88% Jaccard similarity on complex OWL reasoning tasks.
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Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions
Relation operators in knowledge graphs are Kraus channels obeying three axioms, enabling KrausKGE which outperforms baselines on N-to-N relations and supplies a rank-based complexity measure.
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Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs
RALP learns string-based chain-of-thought prompts as scoring functions for knowledge graph triples using Bayesian optimization from fewer than 30 examples, improving link prediction MRR by over 5% and achieving over 88% Jaccard similarity on complex OWL reasoning tasks.