A human-machine method for web table understanding

  • Authors:

  • Guoliang Li

  • Affiliations:

  • Department of Computer Science, Tsinghua Univeristy, Beijing, China

  • Venue:
  • WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
  • Year:
  • 2013

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Abstract

Tabular data on the Web has become a rich source of structured data that is useful for ordinary users to explore. Due to its potential, tables on the Web have recently attracted a number of studies with the goals of understanding the semantics of those Web tables and providing effective search and exploration mechanisms over them. Table understanding is to identify, recognize and interpret tabular structures to enable a variety of tasks such as data extraction, data interpretation, data integration, and search and analysis. In this paper, we propose a human-machine hybrid method for effectively understanding tables on the Web. We develop novel techniques to address four main problems in Web table understanding: Web table extraction, Web table interpretation, Web table integration, and Web table search and analysis. We also discuss some open problems that need more research investigation in Web table understanding. We believe that Web table management will attract much more attention in the coming years.