(Missing) Concept Discovery in Heterogeneous Information Networks

  • Tobias Kötter
  • Michael R. Berthold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250)


This article proposes a new approach to extract existing (or detect missing) concepts from a loosely integrated collection of information units by means of concept graph detection. Thereby a concept graph defines a concept by a quasi bipartite sub-graph of a bigger network with the members of the concept as the first vertex partition and their shared aspects as the second vertex partition. Once the concepts have been extracted they can be used to create higher level representations of the data. Concept graphs further allow the discovery of missing concepts, which could lead to new insights by connecting seemingly unrelated information units.


Information Unit Transaction Database Concept Graph Diverse Domain Concept Discovery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© The Author(s) 2012 2012

Authors and Affiliations

  • Tobias Kötter
    • 1
  • Michael R. Berthold
    • 1
  1. 1.Nycomed-Chair for Bioinformatics and Information MiningUniversity of KonstanzKonstanzGermany

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