Department of Public Health Sciences, College of Behavioral, Social, and Health Sciences, Clemson University, Clemson, SC, USA.
School of Computing, College of Engineering, Computing and Applied Science, Clemson University, Clemson, SC, USA.
Stud Health Technol Inform. 2024 Aug 22;316:1338-1342. doi: 10.3233/SHTI240660.
Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process. We introduce the overall architecture of the active learning pipeline and present some preliminary results. This work is a critical and complementary component in addition to manually building the ontology, especially during the long-term maintenance stage.
本体论对于实现生物医学领域及其他领域的健康信息和信息技术应用互操作性至关重要。传统上，本体论的构建是由人类领域专家（HDE）手动完成的。在这里，我们探索了一种主动学习方法，从出版物中自动识别候选术语，然后手动验证作为深度学习模型训练和学习过程的一部分。我们介绍了主动学习管道的总体架构，并给出了一些初步结果。这项工作是除了手动构建本体论之外的一个关键且互补的组成部分，特别是在长期维护阶段。
