Bisociative Knowledge Discovery

Volume 7250 of the series Lecture Notes in Computer Science pp 427-437

Open Access This content is freely available online to anyone, anywhere at any time.

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

  • Dragana MiljkovicAffiliated withJožef Stefan Institute
  • , Vid PodpečanAffiliated withJožef Stefan Institute
  • , Miha GrčarAffiliated withJožef Stefan Institute
  • , Kristina GrudenAffiliated withNational Institute of Biology
  • , Tjaša StareAffiliated withNational Institute of Biology
  • , Marko PetekAffiliated withNational Institute of Biology
  • , Igor MozetičAffiliated withJožef Stefan Institute
  • , Nada LavračAffiliated withJožef Stefan InstituteUniversity of Nova Gorica


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.