The insights from GAIT's use of GNNs for semantic type detection can be applied to other table-related tasks such as schema matching and data integration by leveraging the power of graph-based representations. For schema matching, GNNs can be utilized to capture the relationships between different table schemas and identify similarities or mappings between them. By representing tables as graphs and applying GNNs to learn the structural patterns, the model can effectively match schemas even in complex scenarios. In data integration, GNNs can help in aligning and merging data from heterogeneous sources by understanding the dependencies and relationships between different datasets. This can improve the accuracy and efficiency of data integration processes by leveraging the insights gained from modeling inter-table dependencies in a similar manner to semantic type detection.