Machine Learning for Ontology Alignment

Faten Abbassi, Yousra Hlaoui, Faouzi Ben Charrada

2025

Abstract

This article proposes an ontology alignment approach that combines supervised machine learning models and schema-matching techniques. Our approach analyzes reference ontologies and their alignments provided by OAEI to extract ontological data matrices and confidence vectors. In addition, these ontological data matrices are normalized using normalization techniques to obtain a coherent format for enhancing the accuracy of the alignments. From the normalized data, syntactic and external similarity matrices are generated via individual matchers before being concatenated to build a final similarity matrix representing the correspondences between two ontologies. This matrix and the confidence vector are then used by six machine learning models, such as Logistic Regression, Random Forest Classifier, Neural Network, Linear SVC, K-Neighbors Classifier and Gradient Boosting Classifier, to identify ontological similarities. To evaluate the performances of our approach, we have compared our results with our previous results (Abbassi and Hlaoui, 2024a). The experiments are performed over the reference ontologies of the benchmark and conference tracks based on their reference alignments provided by OAEI.

Paper Citation

in Harvard Style

Abbassi F., Hlaoui Y. and Ben Charrada F. (2025). Machine Learning for Ontology Alignment. In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-742-9, SciTePress, pages 668-675. DOI: 10.5220/0013425700003928

in Bibtex Style

@conference{enase25,
author={Faten Abbassi and Yousra Hlaoui and Faouzi Ben Charrada},
title={Machine Learning for Ontology Alignment},
booktitle={Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2025},
pages={668-675},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013425700003928},
isbn={978-989-758-742-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Machine Learning for Ontology Alignment
SN - 978-989-758-742-9
AU - Abbassi F.
AU - Hlaoui Y.
AU - Ben Charrada F.
PY - 2025
SP - 668
EP - 675
DO - 10.5220/0013425700003928
PB - SciTePress