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Biomedical ontology alignment with BERT

Existing machine learning-based ontology alignment systems often adopt complicated feature engineering or traditional non-contextual word embeddings. However, they are often outrun by the rule-based systems despite the model complexity. This paper proposes a novel ontology alignment system based on a contextual embedding model named BERT, aiming to sufficiently utilize the text semantics implied by ontologies. Our results on two biomedical alignment tasks demonstrate that, despite using the to-be-aligned classes alone as the input, our system outperforms the leading systems: LogMap and AML.