Conversational Ontology Alignment with ChatGPT

TL;DR

This paper assesses ChatGPT's effectiveness in ontology alignment by comparing its naive approach to established benchmarks, revealing insights into its potential and limitations for semantic matching tasks.

Contribution

It introduces a novel evaluation of ChatGPT's naive use for ontology alignment, highlighting its capabilities and challenges compared to specialized methods.

Findings

ChatGPT shows competitive performance in ontology matching tasks.

Naive approach has limitations in accuracy and consistency.

Potential for using LLMs in semantic integration workflows.

Abstract

This study evaluates the applicability and efficiency of ChatGPT for ontology alignment using a naive approach. ChatGPT's output is compared to the results of the Ontology Alignment Evaluation Initiative 2022 campaign using conference track ontologies. This comparison is intended to provide insights into the capabilities of a conversational large language model when used in a naive way for ontology matching, and to investigate the potential advantages and disadvantages of this approach.