使用逻辑约束来验证协作知识图谱中有关疾病爆发的统计信息：以维基数据中的COVID-19流行病学为例。
Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.
作者信息
Turki Houcemeddine, Jemielniak Dariusz, Hadj Taieb Mohamed A, Labra Gayo Jose E, Ben Aouicha Mohamed, Banat Mus'ab, Shafee Thomas, Prud'hommeaux Eric, Lubiana Tiago, Das Diptanshu, Mietchen Daniel
机构信息
Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia.
Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Masovia, Poland.
出版信息
PeerJ Comput Sci. 2022 Sep 29;8:e1085. doi: 10.7717/peerj-cs.1085. eCollection 2022.
Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.
全球紧急研究需要实时传播精确数据。维基数据（Wikidata）是一个以RDF格式提供的协作式且开放许可的知识图谱，它为交换结构化数据提供了一个理想的平台，这些结构化数据可以使用验证模式和机器人编辑进行验证和整合。在这篇研究文章中，我们编目了评估和验证维基数据中与COVID-19流行病学相关部分所需的一组可自动化任务。这些任务评估统计数据，并通过SPARQL（一种语义数据库查询语言）来实现。我们展示了我们评估维基数据中COVID-19结构化非关系信息的方法的效率，以及该方法在更广泛的协作本体和知识图谱中的适用性。通过将我们提出的方法与先前研究所揭示的其他链接网络数据验证方法的特征进行比较，我们展示了该方法的优点和局限性。
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