@inproceedings{lushnei-etal-2026-large,
    title = "Large Language Models as Oracles for Ontology Alignment",
    author = "Lushnei, Sviatoslav  and
      Shumskyi, Dmytro  and
      Shykula, Severyn  and
      Jim{\'e}nez-Ruiz, Ernesto  and
      Garcez, Artur d'Avila",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.110/",
    pages = "2435--2449",
    ISBN = "979-8-89176-380-7",
    abstract = "There are many methods and systems to tackle the ontology alignment problem, yet a major challenge persists in producing high-quality mappings among a set of input ontologies. Adopting a human-in-the-loop approach during the alignment process has become essential in applications requiring very accurate mappings. However, user involvement is expensive when dealing with large ontologies. In this paper, we analyse the feasibility of using Large Language Models (LLM) to aid the ontology alignment problem. LLMs are used only in the validation of a subset of correspondences for which there is high uncertainty. We have conducted an extensive analysis over several tasks of the Ontology Alignment Evaluation Initiative (OAEI), reporting in this paper the performance of several state-of-the-art LLMs using different prompt templates. Using LLMs as resulted in strong performance in the OAEI 2025, achieving the top-2 overall rank in the bio-ml track."
}