# BERTMap: A BERT-based Ontology Alignment System

arXiv: https://arxiv.org/abs/2112.02682 · HTML: https://mlsanity.com/paper/2112.02682/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 5 Dec 2021.

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

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context](https://mlsanity.com/paper/1806.07976/) (2018)
- [Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules](https://mlsanity.com/paper/2003.05370/) (2020)

## Built on by (most cited first, 30 of 46)

- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [OLaLa: Ontology Matching with Large Language Models](https://mlsanity.com/paper/2311.03837/) (2023)
- [Contextual Semantic Embeddings for Ontology Subsumption Prediction](https://mlsanity.com/paper/2202.09791/) (2022)
- [Agent-OM: Leveraging LLM Agents for Ontology Matching](https://mlsanity.com/paper/2312.00326/) (2023)
- [Exploring Large Language Models for Ontology Alignment](https://mlsanity.com/paper/2309.07172/) (2023)
- [Language Model Analysis for Ontology Subsumption Inference](https://mlsanity.com/paper/2302.06761/) (2023)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching](https://mlsanity.com/paper/2205.03447/) (2022)
- [LLMs4OM: Matching Ontologies with Large Language Models](https://mlsanity.com/paper/2404.10317/) (2024)
- [DeepOnto: A Python Package for Ontology Engineering with Deep Learning](https://mlsanity.com/paper/2307.03067/) (2023)
- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking](https://mlsanity.com/paper/2307.01137/) (2023)
- [Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models](https://mlsanity.com/paper/2305.09785/) (2023)
- [OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment](https://mlsanity.com/paper/2503.21902/) (2025)
- [Deep Active Alignment of Knowledge Graph Entities and Schemata](https://mlsanity.com/paper/2304.04389/) (2023)
- [Truveta Mapper: A Zero-shot Ontology Alignment Framework](https://mlsanity.com/paper/2301.09767/) (2023)
- [Ontology Matching with Large Language Models and Prioritized Depth-First Search](https://mlsanity.com/paper/2501.11441/) (2025)
- [Language Models as Ontology Encoders](https://mlsanity.com/paper/2507.14334/) (2025)
- [Large Language Models as Oracles for Ontology Alignment](https://mlsanity.com/paper/2508.08500/) (2025)
- [To Neuro-Symbolic Classification and Beyond by Compiling Description Logic Ontologies to Probabilistic Circuits](https://mlsanity.com/paper/2601.14894/) (2026)
- [The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labs](https://mlsanity.com/paper/2604.01728/) (2026)
- [Contextualized Structural Self-supervised Learning for Ontology Matching](https://mlsanity.com/paper/2310.03840/) (2023)
- [Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies](https://mlsanity.com/paper/2402.15337/) (2024)
- [OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2502.18992/) (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)
- [Linking Symptom Inventories using Semantic Textual Similarity](https://mlsanity.com/paper/2309.04607/) (2023)
- [RelBERT: Embedding Relations with Language Models](https://mlsanity.com/paper/2310.00299/) (2023)
- [Ontology Revision based on Pre-trained Language Models](https://mlsanity.com/paper/2310.18378/) (2023)

## Abstract (arXiv)

Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed rule-based systems especially in an unsupervised setting. In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. It first predicts mappings using a classifier based on fine-tuning the contextual embedding model BERT on text semantics corpora extracted from ontologies, and then refines the mappings through extension and repair by utilizing the ontology structure and logic. Our evaluation with three alignment tasks on biomedical ontologies demonstrates that BERTMap can often perform better than the leading OM systems LogMap and AML.