LLMs4OL: Large Language Models for Ontology Learning
We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs)
for Ontology Learning (OL). LLMs have shown significant advancements in natural
language processing, demonstrating their ability to capture complex language
patterns in different knowledge domains. Our LLMs4OL paradigm investigates the
following hypothesis: \textit{Can LLMs effectively apply their language pattern
capturing capability to OL, which involves automatically extracting and
structuring knowledge from natural language text?} To test this hypothesis, we
conduct a comprehensive evaluation using the zero-shot prompting method. We
evaluate nine different LLM model families for three main OL tasks: term
typing, taxonomy discovery, and extraction of non-taxonomic relations.
Additionally, the evaluations encompass diverse genres of ontological
knowledge, including lexicosemantic knowledge in WordNet, geographical
knowledge in GeoNames, and medical knowledge in UMLS.
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