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VeTo: Expert Set Expansion in Academia

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12246)

Abstract

Expanding a set of known domain experts with new individuals, that have similar expertise, is a problem with many practical applications (e.g., adding new members to a conference program committee). In this work, we study this problem in the context of academic experts and we introduce VeTo, a novel method to effectively deal with it by exploiting scholarly knowledge graphs. In particular, VeTo expands the given set of experts by identifying researchers that share similar publishing habits with them, based on a graph analysis approach. Our experiments show that VeTo is more effective than existing techniques that can be applied to deal with the same problem.

Keywords

  • Expertise retrieval
  • Scholarly knowledge graphs

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Notes

  1. 1.

    The last one may be larger than the others, however it is easy to take this into consideration.

  2. 2.

    We have also conducted experiments using DOC, the alternative graph-based approach proposed in the same paper, however it performed worse in all cases and its results were omitted from the experimental section for presentation reasons.

  3. 3.

    https://github.com/schatzopoulos/HeySim.

  4. 4.

    https://doi.org/10.5281/zenodo.3739316.

  5. 5.

    https://trec.nist.gov/.

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Acknowledgments

We acknowledge support of this work by the project “Moving from Big Data Management to Data Science” (MIS 5002437/3) which is implemented under the Action “Re-inforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund).

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Correspondence to Thanasis Vergoulis .

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Vergoulis, T., Chatzopoulos, S., Dalamagas, T., Tryfonopoulos, C. (2020). VeTo: Expert Set Expansion in Academia. In: Hall, M., Merčun, T., Risse, T., Duchateau, F. (eds) Digital Libraries for Open Knowledge. TPDL 2020. Lecture Notes in Computer Science(), vol 12246. Springer, Cham. https://doi.org/10.1007/978-3-030-54956-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-54956-5_4

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