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.
Knowledge graphs.ACM Computing Surveys (CSUR), 54(4):1–37, 2021
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.