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Efficient Table Embeddings via Self-Supervised Structural-Semantic Graph Autoencoder
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DOI：10.1016/j.ipm.2025.104298.png)
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Abstract
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• We introduce TEA, a framework for learning embeddings from tabular data. • Our TEA framework models complex structural and semantic relationships efficiently. • TEA shows superior efficiency and effectiveness on schema matching/entity resolution.
Keywords:
Table embedding
Schema matching
Entity resolution
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