In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

The ubiquity of data lakes has created fascinating new challenges for data management
research. In this tutorial, we review the state-of-the-art in data management for data lakes …

We propose TUTA, a unified pre-training architecture for understanding generally structured
tables. Noticing that understanding a table requires spatial, hierarchical, and semantic …

Tabular data often refers to data that is organized in a table with rows and columns. We
observe that this data format is widely used on the Web and within enterprise data …

Tables are powerful and popular tools for organizing and manipulating data. A vast number
of tables can be found on the Web, which represent a valuable knowledge resource. The …

Data scientists are tasked with obtaining insights from data. However, suitable data is often
not immediately at hand, and there may be many potentially relevant datasets in a data lake …

The success of deep learning has sparked interest in improving relational table tasks, like
data preparation and search, with table representation models trained on large table …

In 2008, we wrote about WebTables, an effort to exploit the large and diverse set of
structured databases casually published online in the form of HTML tables. The past decade …

Tabular data is a way to structure, organize, and present information conveniently and
effectively. Real-world tables present data in two dimensions by arranging cells in matrices …