| Autor: |
Menad S; Univ Rouen Normandie, Normandie Univ, LITIS UR 4108 F-76000 Rouen, France., Abdeddaïm S; Univ Rouen Normandie, Normandie Univ, LITIS UR 4108 F-76000 Rouen, France., Soualmia LF; Univ Rouen Normandie, Normandie Univ, LITIS UR 4108 F-76000 Rouen, France. |
| Jazyk: |
angličtina |
| Zdroj: |
Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 22; Vol. 316, pp. 771-775. |
| DOI: |
10.3233/SHTI240526 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
