mlsanityData to 7 Oct 2026

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

arXiv 2409.10146 ↗First posted 16 Sep 2024cs.CL cs.AIcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 16 Sep 2024.

Builds on

11 of 33 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  6. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  7. 2024BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domainscited by 299
  8. 2023Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessmentcited by 227
  9. 2023LLMs4OL: Large Language Models for Ontology Learningcited by 75
  10. 2024LLMs4OM: Matching Ontologies with Large Language Modelscited by 22
  11. 2024DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classificationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attention-Based Taxonomies)cited by 3
  2. 2026OntoLearner: A Modular Python Library for Ontology Learning with Large Language Modelscited by 3
  3. 2025NFDI4DS Shared Tasks for Scholarly Document Processingcited by 0
  4. 2025VSPO: Validating Semantic Pitfalls in Ontology via LLM-Based CQ Generationcited by 0
  5. 2026Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Modelscited by 0
  6. 2026SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Textcited by 0
  7. 2026pro-team at LLMs4OL 2026 Tasks Flagship and Reuse: Retrieval-Augmented Generation and Vocabulary-Constrained Filtering for Ontology Learningcited by 0
  8. 2026When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learningcited by 0