Enhancing enterprise knowledge processes via cross-media extraction

  • Authors:

  • Jose Iria;Victoria Uren;Alberto Lavelli;Sebastian Blohm;Aba-sah Dadzie;Thomas Franz;Ioannis Kompatsiaris;Joao Magalhaes;Spiros Nikolopoulos;Christine Preisach;Piercarlo Slavazza

  • Affiliations:

  • The University of Sheffield, Sheffield, United Kingdom;The Open University, Milton Keynes, United Kingdom;Fondazione Bruno Kessler, Trento, Italy;University of Karlsruhe, Karlsruhe, Germany;The University of Sheffield, Sheffield, United Kingdom;University of Koblenz-Landau, Koblenz, Germany;Centre for Research & Technology Hellas, Thessaloniki, Greece;The University of Sheffield, Sheffield, United Kingdom;Centre for Research & Technology Hellas, Thessaloniki, Greece;University of Hildesheim, Hildesheim, Germany;Quinary, Milan, Italy

  • Venue:
  • Proceedings of the 4th international conference on Knowledge capture
  • Year:
  • 2007

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Abstract

In large organizations the resources needed to solve challenging problems are typically dispersed over systems within and beyond the organization, and also in different media. However, there is still the need, in knowledge environments, for extraction methods able to combine evidence for a fact from across different media. In many cases the whole is more than the sum of its parts: only when considering the different media simultaneously can enough evidence be obtained to derive facts otherwise inaccessible to the knowledge worker via traditional methods that work on each single medium separately. In this paper, we present a cross-media knowledge extraction framework specifically designed to handle large volumes of documents composed of three types of media text, images and raw data and to exploit the evidence across the media. Our goal is to improve the quality and depth of automatically extracted knowledge.