Web Scale Knowledge Extraction

Monday, October 24th - 14:00 - 18:00 - Room: Haber

There has been a significant amount of interest recently in automatically creating large-scale knowledge bases from unstructured text. Compared to traditional, manually created representations, these knowledge bases have the advantage of scale and coverage. They often contain tens of millions of propositions, represented using a variety of encodings, from simple binary assertions to more complicated frame-like structures, and are extracted by parsing and analyzing large text corpora. This workshop is designed to gather researchers in the area of building and applying textually mined knowledge bases and to discuss key related issues and applications.

Find the schedule and further information on the workshop's website.

Accepted Workshop Papers

Daniel Gerber and Axel-Cyrille Ngonga Ngomo. Bootstrapping the Linked Data Web

James Fan, Aditya Kalyanpur, J. William Murdock and Branimir K. Boguraev. Mining Knowledge from Large Corpora for Type Coercion in Question Answering

Giuseppe Rizzo and Raphael Troncy. NERD: Evaluating Named Entity Recognition Tools in the Web of Data

Caroline Barrière, Michel Gagnon. Drugs and Disorders: From specialized resources to Web data

Tran Thanh, Yongtao Ma, Natalya F. Noy. Document Annotation Support Using Biomedical Ontologies

Organization

Organizing Committee

Programme Committee

  • Soren Auer, University of Leipzig, Germany
  • Ken Barker, University of Texas, US
  • Peter Clark, Vulcan Inc, US
  • Alfio Gliozzo, IBM Research, US
  • Raphael Hoffman, University of Washington, US
  • Ed Hovy, USC/ISI, US
  • Doo Soon Kim, University of Texas, US
  • Vladimir Kolovski, Novartis, US
  • Rutu Mulkar-mehta, USC/ISI, US
  • Goran Nenadic, University of Manchester, UK
  • Patrick Pantel, Microsoft Research, US
  • Fabien Suchanek, INRIA Saclay, France
  • Martin Theobald, Max-Planck-Institut für Informatik, Germany
  • Peter Yeh, Accenture, US