Ontology alignment, a critical process in the Semantic Web for detecting relationships between different ontologies, has traditionally focused on identifying so-called "simple" 1-to-1 relationships through class labels and properties comparison. The more practically useful exploration of more complex alignments remains a hard problem to automate, and as such is largely underexplored, i.e. in application practice it is usually done manually by ontology and domain experts. Recently, the surge in Natural Language Processing (NLP) capabilities, driven by advancements in Large Language Models (LLMs), presents new opportunities for enhancing ontology engineering practices, including ontology alignment tasks. This paper investigates the application of LLM technologies to tackle the complex ontology alignment challenge. Leveraging a prompt-based approach and integrating rich ontology content so-called modules our work constitutes a significant advance towards automating the complex alignment task.

翻译：本体对齐作为语义网中检测不同本体间关系的关键过程，传统上侧重于通过类别标签和属性比较识别所谓的"简单"一对一关系。更具实际应用价值的复杂对齐探索仍是一个难以自动化的难题，因此在很大程度上尚未得到充分研究——在实际应用中，通常由本体和领域专家手动完成。近年来，随着大语言模型（LLM）的进步，自然语言处理（NLP）能力的激增为本体工程实践（包括本体对齐任务）带来了新的机遇。本文研究了LLM技术在应对复杂本体对齐挑战中的应用。通过采用基于提示的方法并整合丰富的本体内容（即所谓的模块），我们的工作朝着自动完成复杂对齐任务迈出了重要一步。