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 this https URL
Abstract (translated)
本研究解决了在关系表中检测语义行类型的挑战，这是许多现实应用中的关键任务。虽然像BERT这样的语言模型已经提高了预测准确性，但它们的词输入约束限制了同时处理表内和表间信息的效率。我们提出了一种新颖的方法，使用图神经网络（GNNs）建模内表依赖，使语言模型集中关注表间信息。我们所提出的方法不仅超越了现有最先进的算法，而且为各种GNN类型语义类型检测的实用性和功能提供了新颖的见解。代码可在此处访问：https://url
URL
https://arxiv.org/abs/2405.00123
