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Authors: Veselka Boeva 1 ; Milena Angelova 1 and Elena Tsiporkova 2

Affiliations: 1 Technical University of Sofia, Bulgaria ; 2 The Collective Center for the Belgian Technological Industry, Belgium

Keyword(s): Data Mining, Expert Finding, Health Science, Knowledge Management.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Knowledge-Based Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: In this work, we propose enhanced data-driven techniques that optimize expert representation and identify subject experts via automated 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. An expert profile is presented by a description of the topics in which the person is an expert plus the relative levels (weights) of knowledge or experience he/she has in the different 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. The proposed techniques are tested and evaluated on data extracted from PubMed repository.

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Paper citation in several formats:
Boeva, V.; Angelova, M. and Tsiporkova, E. (2017). Data-driven Techniques for Expert Finding. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-220-2; ISSN 2184-433X, SciTePress, pages 535-542. DOI: 10.5220/0006195105350542

@conference{icaart17,
author={Veselka Boeva. and Milena Angelova. and Elena Tsiporkova.},
title={Data-driven Techniques for Expert Finding},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2017},
pages={535-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006195105350542},
isbn={978-989-758-220-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Data-driven Techniques for Expert Finding
SN - 978-989-758-220-2
IS - 2184-433X
AU - Boeva, V.
AU - Angelova, M.
AU - Tsiporkova, E.
PY - 2017
SP - 535
EP - 542
DO - 10.5220/0006195105350542
PB - SciTePress

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