Authors:
Faten Abbassi
1
;
Yousra Hlaoui
1
and
Faouzi Ben Charrada
2
Affiliations:
1
LIPSIC Laboratory, University of Tunis El Manar Tunis, Tunisia
;
2
LIMTIC Laboratory, University of Tunis El Manar Tunis, Tunisia
Keyword(s):
Machine Learning, Normalization Techniques, Reference Ontologies, Conference Track, Benchmark Track.
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 res
ults 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.
(More)