Text based knowledge discovery with information flow analysis

  • Authors:
  • Dawei Song;Peter Bruza

  • Affiliations:
  • Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom;Queensland University of Technology

  • Venue:
  • APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
  • Year:
  • 2006

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Abstract

Information explosion has led to diminishing awareness: disciplines are becoming increasingly specialized; individuals and groups are becoming ever more insular. This paper considers how awareness can be enhanced via text-based knowledge discovery. Knowledge representation is motivated from a socio-cognitive perspective. Concepts are represented as vectors in a high dimensional semantic space automatically derived from a text corpus. Information flow computation between vectors is proposed as a means of discovering implicit associations between concepts. The potential of information flow analysis in text based knowledge discovery has been demonstrated by two case studies: literature-based scientific discovery by attempting to simulate Swanson’s Raynaud-fish oil discovery in medical texts; and automatic category derivation from document titles. There is some justification to believe that the techniques create awareness of new knowledge.