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.

本文提出了 BERTMap，它是一种可以支持无监督和半监督设置的本体映射系统，它使用 fine-tuning 的 BERT 语境嵌入模型来预测映射，并通过利用本体结构和逻辑来完善映射，本文通过对生物医学本体上的三个对齐任务的评估表明，BERTMap 通常比领先的 OM 系统 LogMap 和 AML 表现更好。