Bisociative Knowledge Discovery

Volume 7250 of the series Lecture Notes in Computer Science pp 33-50

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

From Information Networks to Bisociative Information Networks

  • Tobias KötterAffiliated withNycomed-Chair for Bioinformatics and Information Mining, University of Konstanz
  • , Michael R. BertholdAffiliated withNycomed-Chair for Bioinformatics and Information Mining, University of Konstanz


The integration of heterogeneous data from various domains without the need for prefiltering prepares the ground for bisociative knowledge discoveries where attempts are made to find unexpected relations across seemingly unrelated domains. Information networks, due to their flexible data structure, lend themselves perfectly to the integration of these heterogeneous data sources. This chapter provides an overview of different types of information networks and categorizes them by identifying several key properties of information units and relations which reflect the expressiveness and thus ability of an information network to model heterogeneous data from diverse domains. The chapter progresses by describing a new type of information network known as bisociative information networks. This kind of network combines the key properties of existing networks in order to provide the foundation for bisociative knowledge discoveries. Finally based on this data structure three different patterns are described that fulfill the requirements of a bisociation by connecting concepts from seemingly unrelated domains.