Article | 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden | Advanced Data-driven Techniques for Mining Expertise Link�ping University Electronic Press Conference Proceedings
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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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30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden
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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.

Keywords: Data mining, expert finding, health science, knowledge management

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

Milena Angelova, Veselka Boeva, Elena Tsiporkova
Advanced Data-driven Techniques for Mining Expertise

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

Milena Angelova, Veselka Boeva, Elena Tsiporkova
Advanced Data-driven Techniques for Mining Expertise
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