Data-driven Techniques for Expert Finding

Veselka Boeva, Milena Angelova, Elena Tsiporkova

2017

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 Harvard Style

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 2: ICAART, ISBN 978-989-758-220-2, pages 535-542. DOI: 10.5220/0006195105350542


in Bibtex Style

@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 2: ICAART,},
year={2017},
pages={535-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006195105350542},
isbn={978-989-758-220-2},
}


in EndNote Style

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