LLMs4OL 2026: Large Language Models for Ontology Learning

Overview Paper

• Hamed Babaei Giglou,  Sören Auer, and Jennifer D'Souza. LLMs4OL 2026 Overview: The 3rd Large Language Models for Ontology Learning Challenge

Long Papers

1. Xinyi Zhao and Jiachen Xu. LaboroAI at LLMs4OL 2026 Tasks Flagship and Reuse: Less is More for LLM-based Ontology Learning.

2. Ryn Gray, Ryan Roche, Jaimie Murdock, Douglas C. Crowder, Portia Cooper, Ena Nayak and Brendan Donohoe. ELLMO at LLMs4OL 2026 Tasks Flagship, Reuse, and Taxonomy: Ontology Learning Across Contexts.

3. Pasquale Lisena, Julien Plu, Oscar Moreno Escobar, Edouard Trouillez and Raphael Troncy. LettRAGraph at LLMs4OL 2026 Task Flagship: Prompt-Guided Ontology Extraction with Ensembling.

4. Shivam Mishra, Dhannu Ram Meena, Muneendra Ojha, Krishna Pratap Singh, and Kuldeep Singh. pro-team at LLMs4OL 2026 Tasks Flagship and Reuse: Retrieval-Augmented Generation and Vocabulary-Constrained Filtering for Ontology Learning.

5. Lvting Liu, Yubin Luo, Ji Qi, Chun Zheng, Chengli Zhong and Yi Zhou. Genuineknowledge-KEOL at LLMs4OL 2026 Tasks Flagship, Reuse, and Taxonomy: Hybrid Training-Free Pipelines for Ontology and Taxonomy Discovery.

6. Chuhan Deng, Yuqi Fan, Can Zeng and Chao Zhang. VUL337-OL at LLMs4OL 2026 Tasks Flagship and Reuse: Task-Native Constraints for Ontology Construction and Extension.

7. Erban Ku. Volt-IQ at LLMs4OL 2026: Constrained Graph Synthesis and Domain-Calibrated Ontology Learning.

8. Matias Vizcaino, Jagdsh Chand, and Andy Symonds. semantic-swingers at LLMs4OL 2026 Tasks Flagship, Reuse, and Taxonomy: Retrieval, Fine-Tuning, and the Seen-Unseen Gap in LLM Ontology Learning.

Short Papers

1. Sondes Bannour Souihi and Sandra Kara. LIST-CortAIx at LLMs4OL 2026 Task Flagship: Stage-wise Design and Evaluation of a Modular LLM Pipeline for Ontology Construction.

2. Jiaqi Jin. XuanJi at LLMs4OL 2026 Task Flagship: A Partial Graph-Oriented System for Taxonomy and Typing.

3. Upal Bhattacharya, Maaike de Boer and Sergey Sosnovsky. HybridAI-Squared at LLMs4OL 2026 Task Reuse: Divide and Conquer does not help LLMs perform Ontology Learning.

4. Miquel Canal Esteve, Yoan Gutiérrez, José Abreu Salas and Fernando Llopis. SEMA at LLMs4OL 2026 Task C: Continual Pretraining on Serialized Ontologies for Cross-Domain Taxonomy Discovery.

5. Guojian Zhao, Yuxuan Tang and Zhao Li. BigDipperAI at LLMs4OL 2026 Task Flagship: Structured Generation of Primitive Ontology Triples with Schema-Aligned Auxiliary Supervision.