Abstract
Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge or predictive models, with limited exploration of the potential of Large Language Models (LLMs). We present the LLMs4OM framework, a novel approach to evaluate the effectiveness of LLMs in OM tasks. This framework utilizes two modules for retrieval and matching, respectively, enhanced by zero-shot prompting across three ontology representations: concept, concept-parent, and concept-children. Through comprehensive evaluations using 20 OM datasets from various domains, we demonstrate that LLMs, under the LLMs4OM framework, can match and even surpass the performance of traditional OM systems, particularly in complex matching scenarios. Our results highlight the potential of LLMs to significantly contribute to the field of OM.
Abstract (translated)
知识集成中的元数据匹配（OM）是一个关键任务，其中对异构知识本体的对齐有助于促进数据互操作性和知识共享。传统的OM系统通常依赖于专家知识或预测模型，对大型语言模型的潜力探索有限。我们提出了LLMs4OM框架，一种评估LLM在OM任务中有效性的新方法。该框架采用两个模块进行检索和匹配，分别通过三个知识表示层的零散提示进行加强：概念、概念父体和概念子体。通过使用各种领域的20个OM数据集进行全面评估，我们证明了LLM在LLMs4OM框架下可以匹配甚至超过传统OM系统的表现，特别是在复杂匹配场景中。我们的结果突出了LLM在OM领域显著贡献的潜力。
URL
https://arxiv.org/abs/2404.10317
