Semantic Interoperability of Heterogeneous Information Systems: A Hybrid Framework for Machine Learning-Assisted Semantic Mapping Recommendation

Abstract

Semantic interoperability is a major challenge in the integration of heterogeneous information systems, where differences in data structures, terminologies, and representations complicate the automatic identification of schema matches. Traditional schema matching approaches, primarily based on rules or lexical similarity measures, have limitations in the face of the increasing complexity of digital environments. This article proposes a hybrid framework to improve the automatic recommendation of semantic mappings by combining lexical, structural, statistical, semantic, and business similarities with a supervised learning model. The framework also incorporates expert validation to ensure the quality of the recommended matches and to feed an iterative process for improving the mapping repository. The evaluation is based on a case study in the health insurance sector. Experimental results show that the XGBoost model achieves the best performance with an F1 score of 89.48% and an AUC of 0.915. Top-k evaluations also highlight excellent recommendation capabilities, with a Hit@1 of 83.51%. The ablation study confirms that combining different similarity families significantly improves recommendation quality. These results demonstrate the value of the proposed framework for enhancing semantic interoperability, facilitating the identification of matches between heterogeneous schemas, and reducing the validation effort required from domain experts.