<![CDATA[Schema matching plays a crucial role in data integration by aligning attributes from heterogeneous sources. Traditional rule-based and statistical techniques often lack the flexibility to adapt to diverse schema structures, requiring extensive manual effort. To address these challenges, this paper introduces MatchIA, a machine learning solution that combines word embeddings and random forest to enhance the accuracy of schema matching. The approach accounts for both structural dependencies and semantic relationships between attributes, enabling more automated, and reliable correspondence identification. Unlike traditional methods, MatchIA leverages pretrained embeddings to capture contextual similarities and integrates learned patterns to generalize across various domains. Experimental evaluations on standard datasets demonstrate that MatchIA achieves a precision of 0.94, a recall of 0.92, and an overall matching accuracy of 0.93, significantly outperforming state-of-the-art methods. These results highlight the potential of MatchIA for real-world applications requiring scalable and automated data interoperability.]]>
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