Bisociative Knowledge Discovery pp 427-437

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250)

Modelling a Biological System: Network Creation by Triplet Extraction from Biological Literature

  • Dragana Miljkovic
  • Vid Podpečan
  • Miha Grčar
  • Kristina Gruden
  • Tjaša Stare
  • Marko Petek
  • Igor Mozetič
  • Nada Lavrač

Abstract

The chapter proposes an approach to support modelling of plant defence response to pathogen attacks. Such models are currently built manually from expert knowledge, experimental results, and literature search, which is a very time consuming process. Manual model construction can be effectively complemented by automated model extraction from biological literature. This work focuses on the construction of triplets in the form of subject-predicate-object extracted from scientific papers, which are used by the Biomine automated graph construction and visualisation engine to create the biological model. The approach was evaluated by comparing the automatically generated graph with a manually developed Petri net model of plant defence. This approach to automated model creation was explored also in a bisociative setting. The emphasis is not on creative knowledge discovery, but rather on specifying and crossing the boundaries of knowledge of individual scientists. This could be used to model the expertise of virtual scientific consortia.

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Copyright information

© The Author(s) 2012 2012

Authors and Affiliations

  • Dragana Miljkovic
    • 1
  • Vid Podpečan
    • 1
  • Miha Grčar
    • 1
  • Kristina Gruden
    • 3
  • Tjaša Stare
    • 3
  • Marko Petek
    • 3
  • Igor Mozetič
    • 1
  • Nada Lavrač
    • 1
    • 2
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.University of Nova GoricaNova GoricaSlovenia
  3. 3.National Institute of BiologyLjubljanaSlovenia

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