Discovering entity mentions that are out of a Knowledge Base (KB) from texts
plays a critical role in KB maintenance, but has not yet been fully explored.
The current methods are mostly limited to the simple threshold-based approach
and feature-based classification; the datasets for evaluation are relatively
rare. In this work, we propose BLINKout, a new BERT-based Entity Linking (EL)
method which can identify mentions that do not have a corresponding KB entity
by matching them to a special NIL entity. To this end, we integrate novel
techniques including NIL representation, NIL classification, and synonym
enhancement. We also propose Ontology Pruning and Versioning strategies to
construct out-of-KB mentions from normal, in-KB EL datasets. Results on four
datasets of clinical notes and publications show that BLINKout outperforms
existing methods to detect out-of-KB mentions for medical ontologies UMLS and
SNOMED CT.

提出了基于 BERT 的实体链接方法 BLINKout，该方法结合了 NIL 表示、NIL 分类和同义词增强等多种技巧，能够识别没有对应知识库实体的提及，并通过本体修剪和版本控制等策略，从正常的知识库内实体链接数据集中构建出不在知识库内的提及。在 4 个医学本体数据集上的实验结果表明，BLINKout 方法在 UMLS 和 SNOMED CT 等医学本体的不在知识库内提及检测方面表现优异。