ROME: A Robust Ontology Matching Method Based on Ensemble Learning

In the era of big data, ontology matching, which determines correspondences between concepts, has become a crucial knowledge-mining task. Existing ontology matching methods typically focus on semantics, structure, and instances, often optimizing datasets within specific domains. However, these methods suffer from limited universality when applied across diverse domains. To address this issue, this paper proposes a Robust Ontology Matching method based on Ensemble Learning (ROME). By integrating multiple machine learning algorithms, ROME improves scalability, robustness, and performance, overcoming the challenges of noisy data and data distribution shifts. The proposed approach employs ensemble learning techniques, such as Bagging and Boosting, to enhance model generalization and achieve superior results in ontology matching. Extensive experiments on human and rat anatomy datasets demonstrate that the ensemble fusion approach outperforms existing methods, yielding better mapping precision, recall, and F1 scores.