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.

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