Abstract

A wealth of human knowledge is expressed in structured tables, across web pages, scientific articles, spreadsheets, and databases. This wealth of knowledge is mirrored by diversity in the vast number of layout structures, content types, formats, and surface forms used to express tables. Recent advances in representation learning and knowledge representation have made progress in exploiting structural regularities in tabular data to unlock this knowledge. In this tutorial, we provide a survey of these advances for a host of table understanding tasks, including table segmentation, semantic typing of cells, transforming tables to knowledge graphs, entity linking, and table retrieval tasks for question answering.
The structure of the tutorial will include three major modules. The first will provide attendees an introduction to the seminal work in organization of data in tables, and cover the major goals and approaches of …

Date

August 14, 2021

Authors

Jay Pujara, Pedro Szekely, Huan Sun, Muhao Chen

Source

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages

4060-4061