LLMs4OL: Large Language Models for Ontology Learning
This work addresses the problem of automatically extracting structured knowledge from text for ontology developers, representing an incremental application of existing LLM methods to a new domain.
The researchers investigated whether large language models (LLMs) can effectively perform ontology learning tasks like term typing, taxonomy discovery, and relation extraction from natural language text, evaluating nine LLM families across diverse knowledge domains including WordNet, GeoNames, and UMLS using zero-shot prompting.
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.