A modular, finetuning-free pipeline combining RAG prompting, zero-shot embedding ensembles, and attention-based graph inference achieves top leaderboard results on the LLMs4OL 2025 ontology learning tasks.
LLMs4OL 2025: The 2nd Large Language Models for Ontology Learning Challenge at the 24th ISWC
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
other 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attention-Based Taxonomies)
A modular, finetuning-free pipeline combining RAG prompting, zero-shot embedding ensembles, and attention-based graph inference achieves top leaderboard results on the LLMs4OL 2025 ontology learning tasks.