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Graph Neural Network Approach to Semantic Type Detection in Tables (2405.00123v1)

Published 30 Apr 2024 in cs.LG and cs.CL

Abstract: This study addresses the challenge of detecting semantic column types in relational tables, a key task in many real-world applications. While LLMs 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 LLMs 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

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