  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.
