Using Logical Constraints To Validate Statistical Information About Covid-19 In Collaborative Knowledge Graphs: The Case Of Wikidata

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.

全球紧急研究亟需精准数据的实时传播。维基数据（Wikidata）是一个采用RDF格式的协作式开源知识图谱，为交换可通过验证模式与机器人编辑进行验证和整合的结构化数据提供了理想平台。在本研究论文中，我们梳理了一套可自动化的任务集，用于评估和验证维基数据中与COVID-19流行病学相关的部分。这些任务用于评估统计数据，并通过SPARQL（语义数据库查询语言）实现。我们展示了所提方法在评估维基数据中COVID-19结构化非关系型信息方面的效率，及其在协作本体与更广泛知识图谱中的适用性。通过与先前研究中揭示的其他链接网络数据验证方法的特征进行对比，我们展示了所提方法的优势与局限性。