# 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. data integration; ontology matching; uncertainty +
Footnote †: journal: Medical  
In the context of this work, knowledge building is seen as the process of combining relational data in order to create rich data spaces in which semantics are formally defined [1]. While data integration aims at establishing a common, unified view of data from different sources, the specification of formal semantics enables a further level of complexity, as the \"meaning\" of data is also represented in a fully machine-interpretable context. Ontology is a classic philosophical concept that is related to the study of \"the nature of being\". It has become an active part of the computer world [9], where it is a rich data model aimed at the formal specification of semantics. It supports the representation and processing of knowledge in a machine-readable format according to a model close to the human one. The adoption of ontologies allows effective solutions for knowledge building, since semantic data are enabled in the Semantic Web [10; 11] and are modeled according to an advanced interoperability model, which is commonly referred to as semantic interoperability [12]. We fully rely on an ontology-based approach to support the data integration process. The benefits of ontology in different application domains are well-known and have been extensively discussed from different perspectives by several contributions [2]. An ad hoc approach to knowledge building is time-consuming, error-prone, and, in general, very expensive. Looking at the increasing complexity and scale of systems, automated and semi-automated data integration processes are becoming more and more relevant to assure effectiveness and performance on a large scale. The automation of the knowledge-building process becomes required once the target system scales up. Ontology matching is a crucial step in the knowledge-building process. It reconciles the differences between ontologies and resolves their heterogeneity problem. A seamless and systematic knowledge-building process may only be considered adequate by assuming manual matching of concepts from the different sources of information. However, since manual matching is far from being scalable, automation becomes a compelling need. Automated ontology matching systems use a similarity computation algorithm to find similarities between ontologies to be integrated. However, as extensively discussed in the rest of this paper, such automation leads to a situation of uncertainty since correctness and accuracy cannot be guaranteed in general terms.