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Interpretable semi-supervised sensor knowledge integration for advancing digital economy.

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Název: Interpretable semi-supervised sensor knowledge integration for advancing digital economy.
Autoři: Yang, Chang-Wei
Zdroj: International Journal of Machine Learning & Cybernetics; Nov2025, Vol. 16 Issue 11, p8977-8989, 13p
Abstrakt: Sensor ontologies play a crucial role in the Digital Economy by enabling seamless integration and utilization of diverse sensor data, but their practical application is hindered by heterogeneity arising from semantic discrepancies and structural variations. Ontology Matching (OM) addresses this challenge by aligning semantically equivalent entities, yet existing methods face limitations in balancing multiple objectives and ensuring interpretability. To tackle these issues, this paper proposes an Interpretable Semi-Supervised Entity Matching (ISEM), which leverages a novel Multi-Objective Evolutionary Algorithm (MOEA) that emphasizes interpretability, and dynamic user guidance. In particular, the designed ISEM integrates user preferences to enhance alignment accuracy, employs a multi-objective optimization model to balance the completeness and correctness of matching results, and utilizes an improved MOEA for high-quality alignments. Experimental results on OAEI's Conference dataset and ten pairs of real sensor OM tasks demonstrate that the proposed approach achieves superior matching performance, advancing sensor knowledge integration for the Digital Economy. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Sensor ontologies play a crucial role in the Digital Economy by enabling seamless integration and utilization of diverse sensor data, but their practical application is hindered by heterogeneity arising from semantic discrepancies and structural variations. Ontology Matching (OM) addresses this challenge by aligning semantically equivalent entities, yet existing methods face limitations in balancing multiple objectives and ensuring interpretability. To tackle these issues, this paper proposes an Interpretable Semi-Supervised Entity Matching (ISEM), which leverages a novel Multi-Objective Evolutionary Algorithm (MOEA) that emphasizes interpretability, and dynamic user guidance. In particular, the designed ISEM integrates user preferences to enhance alignment accuracy, employs a multi-objective optimization model to balance the completeness and correctness of matching results, and utilizes an improved MOEA for high-quality alignments. Experimental results on OAEI's Conference dataset and ten pairs of real sensor OM tasks demonstrate that the proposed approach achieves superior matching performance, advancing sensor knowledge integration for the Digital Economy. [ABSTRACT FROM AUTHOR]
ISSN:18688071
DOI:10.1007/s13042-025-02811-w