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What makes a good collaborative knowledge graph: group composition and quality in Wikidata

What makes a good collaborative knowledge graph: group composition and quality in Wikidata
What makes a good collaborative knowledge graph: group composition and quality in Wikidata
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.
305-322
Springer
Piscopo, Alessandro
0cf9852e-96f2-4658-be4d-c7a5ac330c0d
Phethean, Christopher
270f7f09-f94e-4d74-bfbf-2f2700d1572f
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Piscopo, Alessandro
0cf9852e-96f2-4658-be4d-c7a5ac330c0d
Phethean, Christopher
270f7f09-f94e-4d74-bfbf-2f2700d1572f
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67

Piscopo, Alessandro, Phethean, Christopher and Simperl, Elena (2017) What makes a good collaborative knowledge graph: group composition and quality in Wikidata. In Social Informatics. SocInfo 2017. vol. 10539, Springer. pp. 305-322 . (doi:10.1007/978-3-319-67217-5_19).

Record type: Conference or Workshop Item (Paper)

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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Accepted/In Press date: 3 July 2017
e-pub ahead of print date: 3 September 2017
Venue - Dates: International Conference on Social Informatics, Oxford, United Kingdom, 2017-09-12 - 2017-09-14

Identifiers

Local EPrints ID: 414464
URI: https://eprints.soton.ac.uk/id/eprint/414464
PURE UUID: fc60d36d-1f64-4d22-898c-9dd48e5c5568
ORCID for Alessandro Piscopo: ORCID iD orcid.org/0000-0002-0362-4826
ORCID for Christopher Phethean: ORCID iD orcid.org/0000-0001-7697-6585
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

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Date deposited: 29 Sep 2017 16:31
Last modified: 15 Oct 2019 04:51

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