Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.

PeerJ Computer Science Pub Date : 2022-09-29 eCollection Date: 2022-01-01 DOI:10.7717/peerj-cs.1085

Houcemeddine Turki, Dariusz Jemielniak, Mohamed A Hadj Taieb, Jose E Labra Gayo, Mohamed Ben Aouicha, Mus'ab Banat, Thomas Shafee, Eric Prud'hommeaux, Tiago Lubiana, Diptanshu Das, Daniel Mietchen

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引用次数: 4

Abstract

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.

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利用逻辑约束验证协同知识图中疾病暴发的统计信息:以维基数据中的COVID-19流行病学为例

迫切的全球研究需要实时传播精确的数据。Wikidata是一个以RDF格式提供的协作和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论坛，这些数据可以使用验证模式和bot编辑进行验证和整合。在这篇研究文章中，我们编目了一个可自动化的任务集，用于评估和验证与COVID-19流行病学相关的维基数据部分。这些任务评估统计数据，并使用SPARQL实现，SPARQL是语义数据库的查询语言。我们证明了我们的方法在评估维基数据中关于COVID-19的结构化非关系信息方面的效率，以及它在更广泛的协作本体和知识图中的适用性。通过将我们提出的方法与先前研究揭示的其他验证链接web数据的方法的特征进行比较，我们展示了我们提出的方法的优点和局限性。

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PeerJ Computer Science

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