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Semantic-Aware Expert Partitioning

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Book cover Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8722))

Abstract

In this paper, we present a novel semantic-aware clustering approach for partitioning of experts represented by lists of keywords. A common set of all different keywords is initially formed by pooling all the keywords of all the expert profiles. The semantic distance between each pair of keywords is then calculated and the keywords are partitioned by using a clustering algorithm. Each expert is further represented by a vector of membership degrees of the expert to the different clusters of keywords. The Euclidean distance between each pair of vectors is finally calculated and the experts are clustered by applying a suitable partitioning algorithm.

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Author information

Authors and Affiliations

  1. Computer Systems and Technologies Department, Technical University of Sofia, 4000, Plovdiv, Bulgaria

    Veselka Boeva & Liliana Boneva

  2. ICT & Software Engineering Group, Sirris, 1030, Brussels, Belgium

    Elena Tsiporkova

Authors
  1. Veselka Boeva
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  2. Liliana Boneva
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  3. Elena Tsiporkova
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Editor information

Editors and Affiliations

  1. Institute of Information and Communication, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

  2. Department of Computer Science, Wright State University, Dayton, OH, USA

    Pascal Hitzler

  3. Wright State University, Dayton, OH, USA

    Adila A. Krisnadhi

  4. Higher School of Economics, National Research University, Moscow, Russia

    Sergei O. Kuznetsov

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© 2014 Springer International Publishing Switzerland

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Boeva, V., Boneva, L., Tsiporkova, E. (2014). Semantic-Aware Expert Partitioning. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2014. Lecture Notes in Computer Science(), vol 8722. Springer, Cham. https://doi.org/10.1007/978-3-319-10554-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-10554-3_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10553-6

  • Online ISBN: 978-3-319-10554-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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