用大模型当裁判，精准校验术语对应关系。
Large Language Models as Oracles for Ontology Alignment
- 仅让大模型验证高不确定性的术语匹配对。
- 在生物医学赛道取得OAEI 2025榜单前二。
- 适合需要高精度但人力成本高的对齐任务。
现有多种方法应对本体对齐问题，但生成高质量映射仍具挑战。在需高精度的应用中，引入人工参与成为必要，但处理大规模本体时成本过高。本文探索使用大语言模型（LLM）辅助本体对齐的可行性，仅将LLM用于验证高不确定性匹配项。我们在本体对齐评估倡议（OAEI）多个任务上开展广泛分析，测试了多种前沿LLM在不同提示模板下的表现。结果显示，以LLM作为判断依据的方法在OAEI 2025生物医学赛道中获得总体排名前二，表现优异。
原文摘要 · Abstract (English)
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 Oracles resulted in strong performance in the OAEI 2025, achieving the top-2 overall rank in the bio-ml track.
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