  High-quality Web tables are rich sources of information that can be used to
populate Knowledge Graphs (KG). The focus of this paper is an evaluation of
methods for table-to-class annotation, which is a sub-task of Table
Interpretation (TI). We provide a formal definition for table classification as
a machine learning task. We propose an experimental setup and we evaluate 5
fundamentally different approaches to find the best method for generating
vector table representations. Our findings indicate that although transfer
learning methods achieve high F1 score on the table classification task,
dedicated table encoding models are a promising direction as they appear to
capture richer semantics.
