  Information extraction from semi-structured webpages provides valuable
long-tailed facts for augmenting knowledge graph. Relational Web tables are a
critical component containing additional entities and attributes of rich and
diverse knowledge. However, extracting knowledge from relational tables is
challenging because of sparse contextual information. Existing work linearize
table cells and heavily rely on modifying deep language models such as BERT
which only captures related cells information in the same table. In this work,
we propose a novel relational table representation learning approach
considering both the intra- and inter-table contextual information. On one
hand, the proposed Table Convolutional Network model employs the attention
mechanism to adaptively focus on the most informative intra-table cells of the
same row or column; and, on the other hand, it aggregates inter-table
contextual information from various types of implicit connections between cells
across different tables. Specifically, we propose three novel aggregation
modules for (i) cells of the same value, (ii) cells of the same schema
position, and (iii) cells linked to the same page topic. We further devise a
supervised multi-task training objective for jointly predicting column type and
pairwise column relation, as well as a table cell recovery objective for
pre-training. Experiments on real Web table datasets demonstrate our method can
outperform competitive baselines by +4.8% of F1 for column type prediction and
by +4.1% of F1 for pairwise column relation prediction.
