# OLaLa: Ontology Matching with Large Language Models

arXiv: https://arxiv.org/abs/2311.03837 · HTML: https://mlsanity.com/paper/2311.03837/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [A Contrastive Framework for Neural Text Generation](https://mlsanity.com/paper/2202.06417/) (2022)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [BERTMap: A BERT-based Ontology Alignment System](https://mlsanity.com/paper/2112.02682/) (2021)
- [Language Model Analysis for Ontology Subsumption Inference](https://mlsanity.com/paper/2302.06761/) (2023)
- [Ontology engineering with Large Language Models](https://mlsanity.com/paper/2307.16699/) (2023)
- [The Knowledge Graph Track at OAEI -- Gold Standards, Baselines, and the Golden Hammer Bias](https://mlsanity.com/paper/2002.10283/) (2020)
- [KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching](https://mlsanity.com/paper/2204.13931/) (2022)

## Built on by (most cited first, 30 of 35)

- [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)
- [Magneto: Combining Small and Large Language Models for Schema Matching](https://mlsanity.com/paper/2412.08194/) (2024)
- [OntoChat: a Framework for Conversational Ontology Engineering using Language Models](https://mlsanity.com/paper/2403.05921/) (2024)
- [Do LLMs Really Adapt to Domains? An Ontology Learning Perspective](https://mlsanity.com/paper/2407.19998/) (2024)
- [Towards Complex Ontology Alignment using Large Language Models](https://mlsanity.com/paper/2404.10329/) (2024)
- [Retrieval-Augmented Generation-based Relation Extraction](https://mlsanity.com/paper/2404.13397/) (2024)
- [Matchmaker: Self-Improving Large Language Model Programs for Schema Matching](https://mlsanity.com/paper/2410.24105/) (2024)
- [Accelerating Knowledge Graph and Ontology Engineering with Large Language Models](https://mlsanity.com/paper/2411.09601/) (2024)
- [OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment](https://mlsanity.com/paper/2503.21902/) (2025)
- [Generalizable and Scalable Multistage Biomedical Concept Normalization Leveraging Large Language Models](https://mlsanity.com/paper/2405.15122/) (2024)
- [Ontology Matching with Large Language Models and Prioritized Depth-First Search](https://mlsanity.com/paper/2501.11441/) (2025)
- [Do LLMs Dream of Ontologies?](https://mlsanity.com/paper/2401.14931/) (2024)
- [OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching](https://mlsanity.com/paper/2409.14038/) (2024)
- [LLM+KG@VLDB'24 Workshop Summary](https://mlsanity.com/paper/2410.01978/) (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)
- [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)
- [Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment](https://mlsanity.com/paper/2605.09184/) (2026)
- [OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment](https://mlsanity.com/paper/2408.06310/) (2024)
- [OAEI-LLM-T: A TBox Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching](https://mlsanity.com/paper/2503.21813/) (2025)
- [RELRaE: LLM-Based Relationship Extraction, Labelling, Refinement, and Evaluation](https://mlsanity.com/paper/2507.03829/) (2025)
- [OntoAligner Meets Knowledge Graph Embedding Aligners](https://mlsanity.com/paper/2509.26417/) (2025)
- [Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuring](https://mlsanity.com/paper/2511.20679/) (2025)
- [Structured Decomposition for LLM Reasoning: Cross-Domain Validation and Semantic Web Integration](https://mlsanity.com/paper/2601.01609/) (2026)
- [The Mercurial Top-Level Ontology of Large Language Models](https://mlsanity.com/paper/2405.01581/) (2024)
- [Ontological Relations from Word Embeddings](https://mlsanity.com/paper/2408.00444/) (2024)
- [How Does A Text Preprocessing Pipeline Affect Ontology Matching?](https://mlsanity.com/paper/2411.03962/) (2024)
- [medicX-KG: A Knowledge Graph for Pharmacists' Drug Information Needs](https://mlsanity.com/paper/2506.17959/) (2025)

## Abstract (arXiv)

Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible to incorporate this knowledge in a better way into the matching pipeline. A number of decisions still need to be taken, e.g., how to generate a prompt that is useful to the model, how information in the KG can be formulated in prompts, which Large Language Model to choose, how to provide existing correspondences to the model, how to generate candidates, etc. In this paper, we present a prototype that explores these questions by applying zero-shot and few-shot prompting with multiple open Large Language Models to different tasks of the Ontology Alignment Evaluation Initiative (OAEI). We show that with only a handful of examples and a well-designed prompt, it is possible to achieve results that are en par with supervised matching systems which use a much larger portion of the ground truth.