Ten Years of WebTables

By Michael J. Cafarella, Alon Y. Halevy, Hongrae Lee, Jayant Madhavan, Cong Yu, Daisy Zhe Wang, Eugene Wu. 2018

66 citations recorded by the source corpus.

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 has seen a flurry of research and commercial activities around the WebTables project itself, as well as the broad topic of informal online structured data. In this paper, we 1 will review the WebTables project, and try to place it in the broader context of the decade of work that followed. We will also show how the progress over the past ten years sets up an exciting agenda for the future, and will draw upon many corners of the data management community.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuji Roh
2. Steven Euijong Whang
3. Geon Heo
4. Gerhard Weikum
5. Jae-Gil Lee
6. Hwanjun Song
7. Fabian M. Suchanek
8. Thomas Neumann

In Information Systems

1. Gerhard Weikum
2. Jae-Gil Lee
3. Hwanjun Song
4. Geon Heo
5. Yuji Roh
6. Steven Euijong Whang

In Management Science and Operations Research

1. Gerhard Weikum
2. Jae-Gil Lee
3. Hwanjun Song
4. Geon Heo
5. Yuji Roh
6. Steven Euijong Whang
7. Peter Christen

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “One of the most successful systems is WebTables [24], [25], which automatically”
• Knowledge Graphs 2021: A Data Odyssey (2021, background): “Information extraction from web tables has been addressed for a decade (e.g., [2, 17, 41]), but there is only limited work on the specific theme of quantities (e.g., [31] and our recent work [9, 10]).”
• Data collection and quality challenges in deep learning: a data-centric AI perspective (2021, methodology): “Data discovery is the problem of indexing and searching datasets that exist either in corporate data lakes [63, 157] or the Web [38].”