# LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge

arXiv: https://arxiv.org/abs/2409.10146 · HTML: https://mlsanity.com/paper/2409.10146/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment](https://mlsanity.com/paper/2312.12148/) (2023)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [LLMs4OM: Matching Ontologies with Large Language Models](https://mlsanity.com/paper/2404.10317/) (2024)
- [DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classification](https://mlsanity.com/paper/2408.14236/) (2024)

## Built on by (most cited first, 8 of 8)

- [Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attention-Based Taxonomies)](https://mlsanity.com/paper/2508.19428/) (2025)
- [OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models](https://mlsanity.com/paper/2607.01977/) (2026)
- [NFDI4DS Shared Tasks for Scholarly Document Processing](https://mlsanity.com/paper/2509.22141/) (2025)
- [VSPO: Validating Semantic Pitfalls in Ontology via LLM-Based CQ Generation](https://mlsanity.com/paper/2511.07991/) (2025)
- [Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models](https://mlsanity.com/paper/2607.16201/) (2026)
- [SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text](https://mlsanity.com/paper/2607.21610/) (2026)
- [pro-team at LLMs4OL 2026 Tasks Flagship and Reuse: Retrieval-Augmented Generation and Vocabulary-Constrained Filtering for Ontology Learning](https://mlsanity.com/paper/2608.27101/) (2026)
- [When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning](https://mlsanity.com/paper/2608.31118/) (2026)

## Abstract (arXiv)

This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with the 23rd International Semantic Web Conference (ISWC) to explore the potential of Large Language Models (LLMs) in Ontology Learning (OL), a vital process for enhancing the web with structured knowledge to improve interoperability. By leveraging LLMs, the challenge aims to advance understanding and innovation in OL, aligning with the goals of the Semantic Web to create a more intelligent and user-friendly web. In this paper, we give an overview of the 2024 edition of the LLMs4OL challenge and summarize the contributions.