Graph Neural Network Approach to Semantic Type Detection in Tables

• URL: http://arxiv.org/abs/2405.00123v1
• Date: Tue, 30 Apr 2024 18:17:44 GMT
• Title: Graph Neural Network Approach to Semantic Type Detection in Tables
• Authors: Ehsan Hoseinzade, Ke Wang,
• Abstract summary: This study addresses the challenge of detecting semantic column types in relational tables. We propose a novel approach using Graph Neural Networks (GNNs) to model intra-table dependencies. 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.
• Score: 3.929053351442136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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