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What Makes a Good Collaborative Knowledge Graph: Group Composition and Quality in Wikidata

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Social Informatics(SocInfo 2017)

Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 10539))

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Abstract

Wikidata is a community-driven knowledge graph which has drawn much attention from researchers and practitioners since its inception in 2012. The large user pool behind this project has been able to produce information spanning over several domains, which is openly released and can be reused to feed any information-based application. Collaborative production processes in Wikidata have not yet been explored. Understanding them is key to prevent potentially harmful community dynamics and ensure the sustainability of the project in the long run. We performed a regression analysis to investigate how the contribution of different types of users, i.e. bots and human editors, registered or anonymous, influences outcome quality in Wikidata. Moreover, we looked at the effects of tenure and interest diversity among registered users. Our findings show that a balanced contribution of bots and human editors positively influence outcome quality, whereas higher numbers of anonymous edits may hinder performance. Tenure and interest diversity within groups also lead to higher quality. These results may be helpful to identify and address groups that are likely to underperform in Wikidata. Further work should analyse in detail the respective contributions of bots and registered users.

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Acknowledgements

This project is supported by funding received from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642795 (WDAqua ITN).

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Authors and Affiliations

  1. University of Southampton, Southampton, UK

    Alessandro Piscopo, Chris Phethean & Elena Simperl

Authors
  1. Alessandro Piscopo

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  2. Chris Phethean

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  3. Elena Simperl

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Corresponding author

Correspondence toAlessandro Piscopo.

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

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Piscopo, A., Phethean, C., Simperl, E. (2017). What Makes a Good Collaborative Knowledge Graph: Group Composition and Quality in Wikidata. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_19

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