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Named entity disambiguation (NED), which involves mapping textual mentions tostructured entities, is particularly challenging in the medical domain due tothe presence of rare entities. Existing approaches are limited by the presenceof coarse-grained structural resources in biomedical knowledge bases as well asthe use of training datasets that provide low coverage over uncommon resources.In this work, we address these issues by proposing a cross-domain dataintegration method that transfers structural knowledge from a general textknowledge base to the medical domain. We utilize our integration scheme toaugment structural resources and generate a large biomedical NED dataset forpretraining. Our pretrained model with injected structural knowledge achievesstate-of-the-art performance on two benchmark medical NED datasets: MedMentionsand BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57accuracy points.

Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text