  Relation Extraction (RE) from tables is the task of identifying relations
between pairs of columns of a table. Generally, RE models for this task require
labelled tables for training. These labelled tables can also be generated
artificially from a Knowledge Graph (KG), which makes the cost to acquire them
much lower in comparison to manual annotations. However, unlike real tables,
these synthetic tables lack associated metadata, such as, column-headers,
captions, etc; this is because synthetic tables are created out of KGs that do
not store such metadata. Meanwhile, previous works have shown that metadata is
important for accurate RE from tables. To address this issue, we propose
methods to artificially create some of this metadata for synthetic tables.
Afterward, we experiment with a BERT-based model, in line with recently
published works, that takes as input a combination of proposed artificial
metadata and table content. Our empirical results show that this leads to an
improvement of 9\%-45\% in F1 score, in absolute terms, over 2 tabular
datasets.
