The semantic annotation of tabular data plays a crucial role in various downstream tasks. Previous research has proposed knowledge graph (KG)-based and deep learning-based methods, each with its inherent limitations. KG-based methods encounter difficulties annotating columns when there is no match for column cells in the KG. Moreover, KG-based methods can provide multiple predictions for one column, making it challenging to determine the semantic type with the most suitable granularity for the dataset. This type granularity issue limits their scalability. On the other hand, deep learning-based methods face challenges related to the valuable context missing issue. This occurs when the information within the table is insufficient for determining the correct column type. This paper presents KGLink, a method that combines WikiData KG information with a pre-trained deep learning language model for table column annotation, effectively addressing both type granularity and valuable context missing issues. Through comprehensive experiments on widely used tabular datasets encompassing numeric and string columns with varying type granularity, we showcase the effectiveness and efficiency of KGLink. By leveraging the strengths of KGLink, we successfully surmount challenges related to type granularity and valuable context issues, establishing it as a robust solution for the semantic annotation of tabular data.

翻译：表格数据的语义标注在各类下游任务中扮演着关键角色。先前研究提出了基于知识图谱（KG）的方法和基于深度学习的方法，但各自存在固有局限。基于知识图谱的方法在列单元格无法与知识图谱匹配时，难以完成列标注。此外，基于知识图谱的方法可能为同一列提供多种预测，导致难以确定最适合数据集的语义类型粒度。这种类型粒度问题限制了其可扩展性。另一方面，基于深度学习的方法面临有价值上下文缺失的挑战，即当表格内部信息不足以确定正确列类型时出现的问题。本文提出KGLink，一种将WikiData知识图谱信息与预训练深度学习语言模型相结合进行表格列标注的方法，有效解决了类型粒度与有价值上下文缺失这两类问题。通过在涵盖数值型和字符串型列、且具有不同类型粒度的广泛使用的表格数据集上进行全面实验，我们展示了KGLink的有效性与高效性。通过发挥KGLink的优势，我们成功克服了类型粒度与有价值上下文相关的挑战，使其成为表格数据语义标注的稳健解决方案。