Supervised Machine Learning Models and Schema Matching Techniques for Ontology Alignment

Faten Abbassi, Yousra Hlaoui

2024

Abstract

The diversity of existing representations of the same ontology creates a problem of manipulation of the same knowledge according to any computational domain. Unifying similar ontologies by reducing their degree of heterogeneity seems to be the appropriate solution to this problem. This solution consists of aligning similar ontologies using a set of existing ontology schema-matching techniques. In this paper, we present an approach for ontology alignment based on these techniques and machine learning models. To do so, we have developed a matrix construction method based on ontology matching techniques, namely element matching techniques and structure matching techniques implemented by elementary matchers. Once the matrix is constructed, we apply a composite matcher, which is a classifier to combine the individual degrees of similarity calculated for each pair of ontology elements into a final aggregated similarity value between the two ontologies. This composite matcher is implemented via various supervised machine learning models such as LogisticRegression, GradientBoostingClassifier, GaussianNB and KNeighborsClassifier. To experiment our alignment method and to validate the used learning models, we used the reference ontologies and their alignments for the conference and benchmark tracks provided by the Ontology Alignment Evaluation Initiative (OAEI a).

Paper Citation

in Harvard Style

Abbassi F. and Hlaoui Y. (2024). Supervised Machine Learning Models and Schema Matching Techniques for Ontology Alignment. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-716-0, SciTePress, pages 111-118. DOI: 10.5220/0012708200003838

in Bibtex Style

@conference{keod24,
author={Faten Abbassi and Yousra Hlaoui},
title={Supervised Machine Learning Models and Schema Matching Techniques for Ontology Alignment},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2024},
pages={111-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012708200003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Supervised Machine Learning Models and Schema Matching Techniques for Ontology Alignment
SN - 978-989-758-716-0
AU - Abbassi F.
AU - Hlaoui Y.
PY - 2024
SP - 111
EP - 118
DO - 10.5220/0012708200003838
PB - SciTePress