Bisociative Knowledge Discovery

Volume 7250 of the series Lecture Notes in Computer Science pp 54-65

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

Selecting the Links in BisoNets Generated from Document Collections

  • Marc SegondAffiliated withEuropean Center for Soft Computing
  • , Christian BorgeltAffiliated withEuropean Center for Soft Computing


According to Koestler, the notion of a bisociation denotes a connection between pieces of information from habitually separated domains or categories. In this chapter, we consider a methodology to find such bisociations using a BisoNet as a representation of knowledge. In a first step, we consider how to create BisoNets from several tex- tual databases taken from different domains using simple text-mining techniques. To achieve this, we introduce a procedure to link nodes of a BisoNet and to endow such links with weights, which is based on a new measure for comparing text frequency vectors. In a second step, we try to rediscover known bisociations, which were originally found by a human domain expert, namely indirect relations between migraine and magnesium as they are hidden in medical research articles published before 1987. We observe that these bisociations are easily rediscovered by simply following the strongest links.