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Detecting, Assessing and Monitoring Relevant Topics in Virtual Information Environments
March 2009 (vol. 21 no. 3)
pp. 415-427
Jörg Ontrup, Bielefeld University, Bielefeld
Helge Ritter, Bielefeld University, Bielefeld
Sören W. Scholz, Bielefeld University, Bielefeld
Ralf Wagner, University of Kassel, Kassel
The ability to assess the relevance of topics and related sources in information-rich environments is a key to success when scanning business environments. This paper introduces a hybrid system to support managerial information gathering. The system is made up of three components: (1) a hierarchical hyperbolic SOM for structuring the information environment and visualizing the intensity of news activity with respect to identified topics, (2) a spreading activation network for the selection of the most relevant information sources with respect to an already existing knowledge infrastructure, and (3) measures of interestingness for association rules as well as statistical testing facilitates the monitoring of already identified topics. Embedding the system by a framework describing three modes of human information seeking behavior endorses an active organization, exploration and selection of information that matches the needs of decision makers in all stages of the information gathering process. By applying our system in the domain of the hotel industry we demonstrate how typical information gathering tasks are supported. Moreover, we present an empirical study investigating the effectiveness and efficiency of the visualization framework of our system.

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Index Terms:
Human information processing, Data and knowledge visualization, Clustering, Graphical user interfaces, Search process, Information Search and Retrieval, Information Storage and Retrieval, Information Technology, Text mining, Database Applications, Database Management, Information Technology and Systems
Citation:
Jörg Ontrup, Helge Ritter, Sören W. Scholz, Ralf Wagner, "Detecting, Assessing and Monitoring Relevant Topics in Virtual Information Environments," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 3, pp. 415-427, March 2009, doi:10.1109/TKDE.2008.149
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