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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: Schema matching, a fundamental process in data integration, traditionally employs pairwise comparisons to discern semantic correspondences among elements in disparate schemas. However, recent developments underscore the necessity of concurrent matching of interconnected schemas, termed schema alignment, to reconcile heterogeneous elements. This paper presents SchemaLogix, an innovative machine learning-based approach for schema matching. SchemaLogix addresses challenges such as data scarcity and domain-specific constraints through an inventive bootstrapping method, autonomously generating extensive datasets. Furthermore, SchemaLogix capitalizes on inherent alignment context constraints to optimize learning and improve precision across varied schema structures. Additionally, SchemaLogix incorporates user contributions to validate chosen correspondences, refining outputs based on valuable feedback. Empirical evaluations establish SchemaLogix's superiority over traditional methods, achieving an exceptional maximum S1 score of 0.90. These results offer practical insights for real-world applications, substantially advancing data integration and interoperability endeavors.
Mohamed Raoui, Mohammed Ennaouri, Moulay Hafid El Yazidi and Ahmed Zellou, “SchemaLogix: Advancing Interoperability with Machine Learning in Schema Matching” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150542
@article{Raoui2024,
title = {SchemaLogix: Advancing Interoperability with Machine Learning in Schema Matching},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150542},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150542},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Mohamed Raoui and Mohammed Ennaouri and Moulay Hafid El Yazidi and Ahmed Zellou}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.