# LLM-based Zero-shot Triple Extraction for Automated Ontology Generation from Software Engineering Standards

arXiv: https://arxiv.org/abs/2509.00140 · HTML: https://mlsanity.com/paper/2509.00140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024)
- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [End-to-End Ontology Learning with Large Language Models](https://mlsanity.com/paper/2410.23584/) (2024)
- [Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation](https://mlsanity.com/paper/2112.04539/) (2021)
- [An Automatic Ontology Generation Framework with An Organizational Perspective](https://mlsanity.com/paper/2201.05910/) (2022)

## Abstract (arXiv)

Ontologies have supported knowledge representation and white-box reasoning for decades; thus, the automated ontology generation (AOG) plays a crucial role in scaling their use. Software engineering standards (SES) consist of long, unstructured text (with high noise) and paragraphs with domain-specific terms. In this setting, relation triple extraction (RTE), together with term extraction, constitutes the first stage toward AOG. This work proposes an open-source large language model (LLM)-assisted approach to RTE for SES. Instead of solely relying on prompt-engineering-based methods, this study promotes the use of LLMs as an aid in constructing ontologies and explores an effective AOG workflow that includes document segmentation, candidate term mining, LLM-based relation inference, term normalization, and cross-section alignment. Expert-annotated reference sets at three granularities are constructed and used to evaluate the ontology generated from the study. The results show that it is comparable and potentially superior to the OpenIE method of triple extraction.