OLaLa: Ontology Matching with Large Language Models
This addresses the problem of efficient ontology matching for knowledge integration, but it is incremental as it builds on existing LLM capabilities.
The paper tackled ontology matching by exploring zero-shot and few-shot prompting with large language models on OAEI tasks, achieving results comparable to supervised systems using much less ground truth data.
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.