SMUTF: Schema Matching Using Generative Tags and Hybrid Features

We introduce SMUTF, a unique approach for large-scale tabular data schema matching (SM), which assumes that supervised learning does not affect performance in open-domain tasks, thereby enabling effective cross-domain matching. This system uniquely combines rule-based feature engineering, pre-trained language models, and generative large language models. In an innovative adaptation inspired by the Humanitarian Exchange Language, we deploy 'generative tags' for each data column, enhancing the effectiveness of SM. SMUTF exhibits extensive versatility, working seamlessly with any pre-existing pre-trained embeddings, classification …

cs.ai cs.cl cs.db data domain enabling engineering feature feature engineering features generative hybrid language language models large language large language models performance scale schema supervised learning tabular tabular data tags tasks