Efficient Table Embeddings via Self-Supervised Structural-Semantic Graph Autoencoder

Representing tabular data effectively is difficult due to its structural complexity and semantic nuances. Existing models either inadequately capture these features or suffer from computational inefficiency. This paper presents TEA (Table Embedding Autoencoder), a self-supervised learning framework for tabular data embedding. TEA utilizes a Contextual Tabular Graph representation incorporating crucial table relationships and a specialized Table Graph Autoencoder (TGAE) with multi-facet reconstruction (feature/edge/degree). This design ensures efficient learning of comprehensive structural and semantic embeddings. On eight benchmark datasets, TEA surpasses SOTA tabular models and LLMs, achieving average F-measure improvements of 14 (entity resolution). Crucially, TEA is 4x more computationally efficient than the SOTA model, facilitating large-scale data processing.