Graph Neural Network Approach to Semantic Type Detection in Tables
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916230416302080 |
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| author | Hoseinzade, Ehsan Wang, Ke |
| author_facet | Hoseinzade, Ehsan Wang, Ke |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00123 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network Approach to Semantic Type Detection in Tables Hoseinzade, Ehsan Wang, Ke Machine Learning Computation and Language 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 |
| title | Graph Neural Network Approach to Semantic Type Detection in Tables |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2405.00123 |