# Exploring Large Language Models for Ontology Alignment

arXiv: https://arxiv.org/abs/2309.07172 · HTML: https://mlsanity.com/paper/2309.07172/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 6 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [BERTMap: A BERT-based Ontology Alignment System](https://mlsanity.com/paper/2112.02682/) (2021)
- [Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching](https://mlsanity.com/paper/2205.03447/) (2022)
- [DeepOnto: A Python Package for Ontology Engineering with Deep Learning](https://mlsanity.com/paper/2307.03067/) (2023)
- [Truveta Mapper: A Zero-shot Ontology Alignment Framework](https://mlsanity.com/paper/2301.09767/) (2023)

## Built on by (most cited first, 26 of 26)

- [Agent-OM: Leveraging LLM Agents for Ontology Matching](https://mlsanity.com/paper/2312.00326/) (2023)
- [LLMs4OM: Matching Ontologies with Large Language Models](https://mlsanity.com/paper/2404.10317/) (2024)
- [OntoChat: a Framework for Conversational Ontology Engineering using Language Models](https://mlsanity.com/paper/2403.05921/) (2024)
- [Towards Complex Ontology Alignment using Large Language Models](https://mlsanity.com/paper/2404.10329/) (2024)
- [OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment](https://mlsanity.com/paper/2503.21902/) (2025)
- [Ontology Matching with Large Language Models and Prioritized Depth-First Search](https://mlsanity.com/paper/2501.11441/) (2025)
- [Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis](https://mlsanity.com/paper/2403.17216/) (2024)
- [A Language Model based Framework for New Concept Placement in Ontologies](https://mlsanity.com/paper/2402.17897/) (2024)
- [Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting](https://mlsanity.com/paper/2408.15256/) (2024)
- [OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching](https://mlsanity.com/paper/2409.14038/) (2024)
- [Large Language Models as Oracles for Ontology Alignment](https://mlsanity.com/paper/2508.08500/) (2025)
- [A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challenges](https://mlsanity.com/paper/2602.05883/) (2026)
- [MapperGPT: Large Language Models for Linking and Mapping Entities](https://mlsanity.com/paper/2310.03666/) (2023)
- [KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models](https://mlsanity.com/paper/2507.14032/) (2025)
- [GenOM: Ontology Matching with Description Generation and Large Language Model](https://mlsanity.com/paper/2508.10703/) (2025)
- [OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment](https://mlsanity.com/paper/2408.06310/) (2024)
- [DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classification](https://mlsanity.com/paper/2408.14236/) (2024)
- [OAEI-LLM-T: A TBox Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching](https://mlsanity.com/paper/2503.21813/) (2025)
- [OntoAligner Meets Knowledge Graph Embedding Aligners](https://mlsanity.com/paper/2509.26417/) (2025)
- [Evaluating the effectiveness of LLM-based interoperability](https://mlsanity.com/paper/2510.23893/) (2025)
- [Can Structured Data Reduce Epistemic Uncertainty?](https://mlsanity.com/paper/2410.11141/) (2024)
- [How Does A Text Preprocessing Pipeline Affect Ontology Matching?](https://mlsanity.com/paper/2411.03962/) (2024)
- [BRAINCELL-AID: An Agentic AI Created Brain Cell Type Resource for Community Annotation](https://mlsanity.com/paper/2510.17064/) (2025)
- [VSPO: Validating Semantic Pitfalls in Ontology via LLM-Based CQ Generation](https://mlsanity.com/paper/2511.07991/) (2025)
- [Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation](https://mlsanity.com/paper/2604.04450/) (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)

## Abstract (arXiv)

This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies. To test the zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking into account concept labels and structural contexts. Preliminary findings suggest that LLMs have the potential to outperform existing ontology alignment systems like BERTMap, given careful framework and prompt design.