  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.
