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Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of its Field
January 2012
MiNaPe12
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.
Book chapter
Boris Mirkin, Susana Nascimento, Luís Moniz Pereira
R. Kountchev, K. Nakamatsu
Advances in Reasoning-Based Image Processing Intelligent Systems: Conventional and Intelligent Paradigms
Series on Intelligent Systems Reference Library
Springer-Verlag
-
29
423-454
978-3-642-24692-0
-
-
http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Ch15_Springer.pdf
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Boris Mirkin and Susana Nascimento and Luís Moniz Pereira, Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of its Field, in: R. Kountchev and K. Nakamatsu (eds), Advances in Reasoning-Based Image Processing Intelligent Systems: Conventional and Intelligent Paradigms, Series on Intelligent Systems Reference Library, Springer-Verlag, Vol. 29, ISBN 978-3-642-24692-0, Pag. 423-454, (http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Ch15_Springer.pdf), January 2012.
Boris Mirkin, <a href="/people/members/view.php?code=4d69262d034cb8174d039bea8d970836" class="author">Susana Nascimento</a> and <a href="/people/members/view.php?code=6175f826202ff877fba2ad77784cb9cb" class="author">Luís Moniz Pereira</a>, <b>Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of its Field</b>, in: R. Kountchev and K. Nakamatsu (eds), <u>Advances in Reasoning-Based Image Processing Intelligent Systems: Conventional and Intelligent Paradigms</u>, Series on Intelligent Systems Reference Library, Springer-Verlag, Vol. 29, ISBN 978-3-642-24692-0, Pag. 423-454, (<a href="http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Ch15_Springer.pdf" target="_blank">url</a>), January 2012.
@incollection {MiNaPe12, author = {Boris Mirkin and Susana Nascimento and Lu\'{\i}s Moniz Pereira}, editor = {R. Kountchev and K. Nakamatsu}, title = {Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of its Field}, booktitle = {Advances in Reasoning-Based Image Processing Intelligent Systems: Conventional and Intelligent Paradigms}, series = {Series on Intelligent Systems Reference Library}, publisher = {Springer-Verlag}, volume = {29}, pages = {423-454}, isbn = {978-3-642-24692-0}, url = {http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Ch15_Springer.pdf}, abstract = {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.}, month = {January}, year = {2012}, }
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