BERTMap: A BERT-Based Ontology Alignment System


  • Yuan He University of Oxford
  • Jiaoyan Chen University of Oxford
  • Denvar Antonyrajah Samsung Research UK
  • Ian Horrocks University of Oxford



Knowledge Representation And Reasoning (KRR), Data Mining & Knowledge Management (DMKM), Speech & Natural Language Processing (SNLP)


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.




How to Cite

He, Y., Chen, J., Antonyrajah, D., & Horrocks, I. (2022). BERTMap: A BERT-Based Ontology Alignment System. Proceedings of the AAAI Conference on Artificial Intelligence, 36(5), 5684-5691.



AAAI Technical Track on Knowledge Representation and Reasoning