BERTMap: A BERT-based Ontology Alignment System

• URL: http://arxiv.org/abs/2112.02682v1
• Date: Sun, 5 Dec 2021 21:08:44 GMT
• Title: BERTMap: A BERT-based Ontology Alignment System
• Authors: Yuan He, Jiaoyan Chen, Denvar Antonyrajah, Ian Horrocks
• Abstract summary: BERTMap can support both unsupervised and semi-supervised settings. BERTMap can often perform better than the leading systems LogMap and AML.
• Score: 24.684912604644865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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