Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible to incorporate this knowledge in a better way into the matching pipeline. A number of decisions still need to be taken, e.g., how to generate a prompt that is useful to the model, how information in the KG can be formulated in prompts, which Large Language Model to choose, how to provide existing correspondences to the model, how to generate candidates, etc. In this paper, we present a prototype that explores these questions by applying zero-shot and few-shot prompting with multiple open Large Language Models to different tasks of the Ontology Alignment Evaluation Initiative (OAEI). We show that with only a handful of examples and a well-designed prompt, it is possible to achieve results that are en par with supervised matching systems which use a much larger portion of the ground truth.

翻译：本体（更广义地：知识图谱）匹配是一项具有挑战性的任务，其中自然语言信息是最重要的处理信号之一。随着大型语言模型的兴起，我们能够更好地将这种知识融入匹配流程中。然而，仍需做出诸多决策，例如：如何生成对模型有效的提示、如何在提示中表述知识图谱中的信息、选择何种大型语言模型、如何向模型提供现有对应关系、以及如何生成候选匹配等。本文提出一个原型系统，通过将零样本和少样本提示与多个开源大型语言模型相结合，探索了上述问题，并应用于本体对齐评估倡议（OAEI）的各项任务。实验表明，仅需少量示例和精心设计的提示，即可取得与使用更大比例真实标注数据的监督匹配系统相媲美的结果。