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This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies.
BERTMap: A BERT-based ontology alignment system,
Y. He, J. Chen, D. Antonyrajah, I. Horrocks, · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback,
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al., · 2022
Earlier work this paper cites.
Scaling instruction-finetuned language models,
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, E. Li, X. Wang, M. Dehghani, S. Brahma, et al., · 2022
Cited alongside, same era.
Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching,
Y. He, J. Chen, H. Dong, E. Jiménez-Ruiz, A. Hadian, I. Horrocks, · 2022
Cited alongside, same era.
Truveta mapper: A zero-shot ontology alignment framework,
M. Amir, M. Baruah, M. Eslamialishah, S. Ehsani, A. Bahramali, S. Naddaf-Sh, S. Zarandioon, · 2023
Closest in time.
Deeponto: A python package for ontology engineering with deep learning,
Y. He, J. Chen, H. Dong, I. Horrocks, C. Allocca, T. Kim, B. Sapkota, · 2023
Closest in time.
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