Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation

Abstract

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such processes by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective by looking at ontology matching techniques. As the manual matching of different sources of information becomes unrealistic once the system scales up, the automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual non-semantically enriched relational data with the support of existing tools (pre-LLM technology) for automatic ontology matching from the scientific community. Even considering a relatively simple case study—i.e., the spatio–temporal alignment of macro indicators—outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for more generalized application.