BioSTransformers for Biomedical Ontologies Alignment

Safaa Menad, Wissame Laddada, Saïd Abdeddaïm, Lina Soualmia

2023

Abstract

This paper aims at describing the new siamese neural models that we have developed. They optimize a self supervised contrastive learning function on scientific biomedical literature articles. The results obtained on several benchmarks show that the proposed models are able to improve various biomedical tasks without examples (zero shot) and are comparable to biomedical transformers fine-tuned on supervised data specific to the problems addressed. Moreover, these new siamese models are exploited to align biomedical ontologies, demonstrating their semantic mapping capabilities. We then compare the different approaches of alignments that we have proposed. In conclusion, we propose a distinct methods and data sources that we evaluate and compare to validate our alignments.

Paper Citation

in Harvard Style

Menad S., Laddada W., Abdeddaïm S. and Soualmia L. (2023). BioSTransformers for Biomedical Ontologies Alignment. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-671-2, SciTePress, pages 73-84. DOI: 10.5220/0012188600003598

in Bibtex Style

@conference{keod23,
author={Safaa Menad and Wissame Laddada and Saïd Abdeddaïm and Lina Soualmia},
title={BioSTransformers for Biomedical Ontologies Alignment},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2023},
pages={73-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012188600003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - BioSTransformers for Biomedical Ontologies Alignment
SN - 978-989-758-671-2
AU - Menad S.
AU - Laddada W.
AU - Abdeddaïm S.
AU - Soualmia L.
PY - 2023
SP - 73
EP - 84
DO - 10.5220/0012188600003598
PB - SciTePress