Browsing by Author "Das, Diptanshu"
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Item Representing Covid-19 Information In Collaborative Knowledge Graphs: The Case Of Wikidata(2020-09-14) Turki, Houcemeddine; Hadj Taieb, Mohamed Ali; Shafee, Thomas; Lubiana, Tiago; Jemielniak, Dariusz; Ben Aouicha, Mohamed; Labra-Gayo, Jose Emilio; Youngstrom, Eric Arden; Banat, Mus'ab; Das, Diptanshu; Mietchen, DanielInformation related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored and analyzed for purposes like decision support as well as educational and scholarly research.Item Using Logical Constraints To Validate Statistical Information About Covid-19 In Collaborative Knowledge Graphs: The Case Of Wikidata(2020-08-30) Turki, Houcemeddine; Jemielniak, Dariusz; Hadj Taieb, Mohamed Ali; Labra-Gayo, Jose Emilio; Ben Aouicha, Mohamed; Banat, Mus'ab; Shafee, Thomas; Prud'hommeaux, Eric; Lubiana Alves, Tiago; Das, Diptanshu; Mietchen, DanielUrgent 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.