TCN: Table Convolutional Network for Web Table Interpretation

TL;DR

This paper introduces TCN, a novel table convolutional network that effectively captures both intra- and inter-table contextual information for web table interpretation, significantly improving knowledge extraction accuracy.

Contribution

The work proposes a new relational table representation learning approach with attention-based aggregation modules and a multi-task training framework, advancing web table understanding.

Findings

Outperforms baselines with +4.8% F1 in column type prediction.

Achieves +4.1% F1 improvement in column relation prediction.

Demonstrates effectiveness on real web table datasets.

Abstract

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.