Abstract
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.
Abstract (translated)
知识图谱对齐，作为一个在语义网中检测不同知识图谱之间关系的关键过程，通常集中在通过类标签和属性比较识别所谓的“简单”1对1关系。更实际可行的对更复杂对齐的探索仍然是一个难以自动化的困难问题，因此它仍然被大大忽视。即在应用实践中，通常是由本体和领域专家手动完成的。最近，自然语言处理（NLP）能力的突飞猛进，受到大型语言模型（LLMs）的进步，为增强语义工程实践提供了新的机会，包括语义对齐任务。本文调查了LLM技术在解决复杂语义对齐挑战中的应用。我们利用提示式方法并整合了丰富的语义内容，所谓的模块，这使得我们的工作在自动解决复杂对齐任务方面取得了显著的进展。
URL
https://arxiv.org/abs/2404.10329
