Univ Rouen Normandie, Normandie Univ, LITIS UR 4108 F-76000 Rouen, France.
Stud Health Technol Inform. 2024 Aug 22;316:771-775. doi: 10.3233/SHTI240526.
Ontologies play a key role in representing and structuring domain knowledge. In the biomedical domain, the need for this type of representation is crucial for structuring, coding, and retrieving data. However, available ontologies do not encompass all the relevant concepts and relationships. In this paper, we propose the framework SiMHOMer (Siamese Models for Health Ontologies Merging) to semantically merge and integrate the most relevant ontologies in the healthcare domain, with a first focus on diseases, symptoms, drugs, and adverse events. We propose to rely on the siamese neural models we developed and trained on biomedical data, BioSTransformers, to identify new relevant relations between concepts and to create new semantic relations, the objective being to build a new merging ontology that could be used in applications. To validate the proposed approach and the new relations, we relied on the UMLS Metathesaurus and the Semantic Network. Our first results show promising improvements for future research.
本体在表示和构建领域知识方面起着关键作用。在生物医学领域，这种表示形式对于结构、编码和检索数据至关重要。然而，现有的本体并不能包含所有相关的概念和关系。在本文中，我们提出了 SiMHOMer（用于健康本体合并的孪生模型）框架，以语义上合并和整合医疗保健领域中最相关的本体，首先关注疾病、症状、药物和不良事件。我们建议依赖我们在生物医学数据上开发和训练的孪生神经模型 BioSTransformers，以识别概念之间的新的相关关系，并创建新的语义关系，目标是构建一个可用于应用程序的新的合并本体。为了验证所提出的方法和新关系，我们依赖于 UMLS Metathesaurus 和语义网络。我们的初步结果显示了未来研究的有希望的改进。
