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Matching Large Biomedical Ontologies Using Symbolic Regression

Published: 30 December 2021 Publication History

Abstract

The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Matching methods are of great importance since they allow us to find the pivot points from which an automatic data integration process can be established. Unlike the most recent developments based on deep learning, this study presents our research on the development of new methods for ontology matching that are accurate and interpretable at the same time. For this purpose, we rely on a symbolic regression model specifically trained to find the mathematical expression that can solve the ground truth accurately, with the possibility of being understood by a human operator and forcing the processor to consume as little energy as possible. The experimental evaluation results show that our approach seems to be promising.

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  • (2025)Anchor-based ontology partitioning and Genetic Programming with Relevance Reasoning for large-scale biomedical ontology matchingExpert Systems with Applications10.1016/j.eswa.2025.126445270(126445)Online publication date: Apr-2025
  • (2024)Efficient ontology matching through compact linear genetic programming with surrogate-assisted local searchSwarm and Evolutionary Computation10.1016/j.swevo.2024.10175891(101758)Online publication date: Dec-2024
  • (2024)Automatic Knowledge Graph matching via Self-adaptive Designed Genetic ProgrammingKnowledge-Based Systems10.1016/j.knosys.2024.111628293:COnline publication date: 7-Jun-2024
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        cover image ACM Other conferences
        iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence
        November 2021
        658 pages
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        Publication History

        Published: 30 December 2021

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        Author Tags

        1. Information Integration
        2. Ontology Matching
        3. Similarity Measures

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        • French Ministry of Foreign and European Affairs
        • French Ministry of Higher Education and Research
        • Austrian Agency for International Cooperation in Education and Research

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        View all
        • (2025)Anchor-based ontology partitioning and Genetic Programming with Relevance Reasoning for large-scale biomedical ontology matchingExpert Systems with Applications10.1016/j.eswa.2025.126445270(126445)Online publication date: Apr-2025
        • (2024)Efficient ontology matching through compact linear genetic programming with surrogate-assisted local searchSwarm and Evolutionary Computation10.1016/j.swevo.2024.10175891(101758)Online publication date: Dec-2024
        • (2024)Automatic Knowledge Graph matching via Self-adaptive Designed Genetic ProgrammingKnowledge-Based Systems10.1016/j.knosys.2024.111628293:COnline publication date: 7-Jun-2024
        • (2024)Efficient large-scale biomedical ontology matching with anchor-based biomedical ontology partitioning and compact geometric semantic genetic programmingJournal of Industrial Information Integration10.1016/j.jii.2024.10063741(100637)Online publication date: Sep-2024

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