Advanced Data-driven Techniques for Mining Expertise

Milena Angelova
Technical University of Sofia-branch Plovdiv, Bulgaria

Veselka Boeva
Blekinge Institute of Technology, Karlskrona, Sweden

Elena Tsiporkova
Sirris, The Collective Center for the Belgian technological industry, Brussels, Belgium

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Ingår i: 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden

Linköping Electronic Conference Proceedings 137:5, s. 45-52

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Publicerad: 2017-05-12

ISBN: 978-91-7685-496-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


In this work, we discuss enhanced techniques that optimize expert representation and identify subject experts via clustering analysis of the available online information. We use a weighting method to assess the levels of expertise of an expert to the domain-specific topics. In this context, we define a way to estimate the expertise similarity between experts. Then the experts finding task is viewed as a list completion task and techniques that return similar experts to ones provided by the user are considered. In addition, we discuss a formal concept analysis approach for clustering of a group of experts with respect to given subject areas. The produced grouping of experts can further be used to identify individuals with the required competence.


Data mining, expert finding, health science, knowledge management


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