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Bisociative Knowledge Discovery pp 54–65Cite as

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Selecting the Links in BisoNets Generated from Document Collections

Selecting the Links in BisoNets Generated from Document Collections

  • Marc Segond5 &
  • Christian Borgelt5 
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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7250)

Abstract

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.

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Authors and Affiliations

  1. European Center for Soft Computing, Calle Gonzalo Gutiérrez Quirós s/n, E-33600, Mieres, Asturias, Spain

    Marc Segond & Christian Borgelt

Authors
  1. Marc Segond
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  2. Christian Borgelt
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Editors and Affiliations

  1. Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Michael R. Berthold

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Segond, M., Borgelt, C. (2012). Selecting the Links in BisoNets Generated from Document Collections. In: Berthold, M.R. (eds) Bisociative Knowledge Discovery. Lecture Notes in Computer Science(), vol 7250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31830-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-31830-6_5

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