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

机译：什么使一个好的协作知识图：Wikidata中的组组成和质量

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摘要

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.
机译：自2012年成立以来，Wikidata是一个社区驱动的知识图，受到了研究人员和从业人员的广泛关注。该项目背后的庞大用户群已能够产生跨越多个域的信息，该信息已公开发布并可以重复使用。喂任何基于信息的应用程序。 Wikidata中的协同生产过程尚未探索。了解它们是防止潜在有害的社区动态并确保该项目的长期可持续性的关键。我们进行了回归分析，以调查注册或匿名的不同类型的用户（即机器人和人工编辑）的贡献如何影响Wikidata中的结果质量。此外，我们研究了注册用户使用权和利益多元化的影响。我们的发现表明，机器人和人工编辑的均衡贡献对结果质量产生积极影响，而匿名编辑的数量增加可能会影响性能。群体内部的任期和利益多样性也可以提高质量。这些结果可能有助于识别和解决可能在Wikidata中表现不佳的组。进一步的工作应详细分析机器人和注册用户的各自贡献。

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