# From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development

arXiv: https://arxiv.org/abs/2602.00699 · HTML: https://mlsanity.com/paper/2602.00699/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)
- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [Towards Ontology Construction with Language Models](https://mlsanity.com/paper/2309.09898/) (2023)
- [The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies](https://mlsanity.com/paper/2404.03044/) (2024)
- [A Short Review for Ontology Learning: Stride to Large Language Models Trend](https://mlsanity.com/paper/2404.14991/) (2024)
- [Semi-Automatic Terminology Ontology Learning Based on Topic Modeling](https://mlsanity.com/paper/1709.01991/) (2017)

## Built on by (most cited first, 1 of 1)

- [When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning](https://mlsanity.com/paper/2608.31118/) (2026)

## Abstract (arXiv)

Ontologies are essential for structuring domain knowledge, improving accessibility, sharing, and reuse. However, traditional ontology construction relies on manual annotation and conventional natural language processing (NLP) techniques, making the process labour-intensive and costly, especially in specialised fields like casting manufacturing. The rise of Large Language Models (LLMs) offers new possibilities for automating knowledge extraction. This study investigates three LLM-based approaches, including pre-trained LLM-driven method, in-context learning (ICL) method and fine-tuning method to extract terms and relations from domain-specific texts using limited data. We compare their performances and use the best-performing method to build a casting ontology that validated by domian expert.