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

## TL;DR

This paper presents an LLM-assisted approach for relation triple extraction from software engineering standards to automate ontology generation, demonstrating comparable or superior performance to existing methods.

## Contribution

It introduces a novel LLM-based workflow for automated ontology generation from unstructured software engineering standards texts.

## Key findings

- LLM-assisted method achieves high accuracy in relation triple extraction.
- The approach outperforms traditional OpenIE methods in certain metrics.
- The workflow effectively handles noisy, domain-specific text for ontology creation.

## Abstract

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.