  This study addresses the challenge of detecting semantic column types in
relational tables, a key task in many real-world applications. While language
models like BERT have improved prediction accuracy, their token input
constraints limit the simultaneous processing of intra-table and inter-table
information. We propose a novel approach using Graph Neural Networks (GNNs) to
model intra-table dependencies, allowing language models to focus on
inter-table information. Our proposed method not only outperforms existing
state-of-the-art algorithms but also offers novel insights into the utility and
functionality of various GNN types for semantic type detection. The code is
available at https://github.com/hoseinzadeehsan/GAIT
