# A Short Review for Ontology Learning: Stride to Large Language Models Trend

arXiv: https://arxiv.org/abs/2404.14991 · HTML: https://mlsanity.com/paper/2404.14991/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2024.

## Built on by (most cited first, 5 of 5)

- [Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning](https://mlsanity.com/paper/2504.07640/) (2025)
- [From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development](https://mlsanity.com/paper/2602.00699/) (2026)
- [Specific Domain Ontology Construction Using Large Language Models](https://mlsanity.com/paper/2606.20691/) (2026)
- [Natural Language to First-Order Logic LLM-based Autoformalization](https://mlsanity.com/paper/2610.12030/) (2026)
- [DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models](https://mlsanity.com/paper/2610.18635/) (2026)

## Abstract (arXiv)

Ontologies provide formal representation of knowledge shared within Semantic Web applications. Ontology learning involves the construction of ontologies from a given corpus. In the past years, ontology learning has traversed through shallow learning and deep learning methodologies, each offering distinct advantages and limitations in the quest for knowledge extraction and representation. A new trend of these approaches is relying on large language models (LLMs) to enhance ontology learning. This paper gives a review in approaches and challenges of ontology learning. It analyzes the methodologies and limitations of shallow-learning-based and deep-learning-based techniques for ontology learning, and provides comprehensive knowledge for the frontier work of using LLMs to enhance ontology learning. In addition, it proposes several noteworthy future directions for further exploration into the integration of LLMs with ontology learning tasks.