@misc{heExploringLargeLanguage2023,
  title = {Exploring {{Large Language Models}} for {{Ontology Alignment}}},
  author = {He, Yuan and Chen, Jiaoyan and Dong, Hang and Horrocks, Ian},
  year = {2023},
  month = sep,
  number = {arXiv:2309.07172},
  eprint = {2309.07172},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2309.07172},
  urldate = {2024-03-12},
  abstract = {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. To test the zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking into account concept labels and structural contexts. Preliminary findings suggest that LLMs have the potential to outperform existing ontology alignment systems like BERTMap, given careful framework and prompt design.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning},
  groups = {Ontologies and AI},
  timestamp = {2024-03-12T15:15:00Z},
  file = {heExploringLargeLanguage2023.pdf:/home/upal/Zotero/storage/SHGN7XCU/heExploringLargeLanguage2023.pdf:application/pdf;arXiv.org Snapshot:/home/upal/Zotero/storage/4K9JWLHU/2309.html:text/html}
}

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