Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of Its Field

Part of the Intelligent Systems Reference Library book series (ISRL, volume 29)


We describe a novel method for the analysis of research activities of an organization by mapping that to a taxonomy tree of the field. The method constructs fuzzy membership profiles of the organizationmembers or teams in terms of the taxonomy’s leaves (research topics), and then it generalizes them in two steps. These steps are: (i) fuzzy clustering research topics according to their thematic similarities in the department, ignoring the topology of the taxonomy, and (ii) optimally lifting clusters mapped to the taxonomy tree to higher ranked categories by ignoring “small” discrepancies. We illustrate the method by applying it to data collected by using an in-house e-survey tool from a university department and from a university research center. The method can be considered for knowledge generalization over any taxonomy tree.


Fuzzy Cluster Fuzzy Membership Spectral Cluster Subject Cluster Fuzzy Cluster Method 
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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© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceBirkbeck University of LondonLondonUK
  2. 2.School of Applied Mathematics and InformaticsHigher School of EconomicsMoscowRF
  3. 3.Department of Computer Science and Centre for Artificial Intelligence (CENTRIA), Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal

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