Graph Neural Network Approach to Semantic Type Detection in Tables
<|reference_start|>Graph Neural Network Approach to Semantic Type Detection in Tables: 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<|reference_end|>
@article{hoseinzade2024graph,
    title={Graph Neural Network Approach to Semantic Type Detection in Tables},
    author={Ehsan Hoseinzade, Ke Wang},
    journal={In Pacific-Asia Conference on Knowledge Discovery and Data Mining,
  pp. 121-133. Singapore: Springer Nature Singapore, 2024},
    year={2024},
    doi={10.1007/978-981-97-2266-2_10},
    archivePrefix={arXiv},
    eprint={2405.00123},
    primaryClass={cs.LG cs.CL}
}