Masters Thesis
University of Georgia (Dec. 2007)
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SEMEF: A Taxonomy-Based Discovery of Experts, Expertise and Collaboration Networks

Delroy Cameron

Dr. I. Budak Arpinar
Dr. Prashant Doshi
Dr. Robert J. Woods

Abstract: Finding relevant experts in research is often critical and essential for collaboration. Semantics can refine the level of granul arity at which the expertise of various experts can be determined, by explicitly expressing relationships between topics and var ious subtopics using a taxonomy. Such topic-subtopics relationships allow extrapolation of expertise, based on the notion that e xpertise in subtopics is also indicative of expertise in a topic itself. Additionally, a taxonomy enables enrichment of research er Expertise Profiles, based on explicit relationships between the topics of a publication and topic-subtopics relationships in the taxonomy. We describe an approach that uses semantics to find experts, expertise as well as collaboration networks, in a Pee r-Review setting, using the implicit coauthorship network of the DBLP bibliography and a taxonomy of Computer Science topics. Va rious collaboration levels, based on degrees of separation, create the added dimension of presenting potentially unknown experts , also qualified for Program Committee (PC) membership, to the PC Chair(s).

Index Words: C-Net, Collaboration Level, Collaboration Strength, Collaboration Network, Geodesic, Expert Finder, Expertise Profile, Semantic Association, Semantic Web, Taxonomy

  • Word (DOC)
  • PDF (PDF)
  • PPT (PPT)
  • Dataset
    SwetoDblp (560,792 authors)
    Taxonomy of Topics (320 Topics)
    Papers-to-Topics Dataset (7MB zipped file)
    Citeseer Publication Impact Statistics (1221 Venues)