Representing COVID-19 information in collaborative knowledge graphs: The case of Wikidata

Abstract

Information 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.

WikiProject Clinical Trials for Wikidata

“…The profile “Clinical trials for Zika fever” which is the model profile for “clinical trials by medical condition” lists those trials where the main subject (property P921) is Zika fever (item Q8071861). This medical condition was chosen because of development with a related project, WikiProject Zika Corpus, which profiled Zika virus in Wikidata [61] and developed norms in managing medical publications on Wikidata, thereby also paving the way for addressing the current COVID-19 pandemic in a systematic fashion [19]. ClinWiki contributed structured data and guidance to WikiProject Clinical Trials for organizing volunteers to apply topic tags of patient interest to clinical trial records.…”

“…One difference between the projects, though not necessarily the most significant, is that the community at WikiProject COVID-19 has its own collection of queries and datasets which raise different issues that those of this project. [19]. Lowering access barriers to clinical trials data could have unexpected social and ethical consequences.…”

“…[13]. On that basis, since the launch of Wikidata, [14] similar research has been undertaken on its quality, [15] including its medical content [16,17,18,19], multilingual aspects [20,21] and the potential for integration with research workflows. [22,23,24,25,1] The Wikimedia editorial community has created and maintains a governance process which uses consensus of participants as the basis of authority.…”

“…[13]. On that basis, since the launch of Wikidata, [14] similar research has been undertaken on its quality, [15] including its medical content [16, 17, 18, 19], multilingual aspects [20, 21] and the potential for integration with research workflows. [22, 23, 24, 25, 1]…”

Data Models for Annotating Biomedical Scholarly Publications: the Case of CORD-19

“…The high volume of unstructured information available on COVID-19 is hard to process without sophisticated infrastructure [48] and complex computational models, based on machine learning and natural language processing techniques [50]. Alternatively, leveraging semantically structured representations of knowledge, such as Wikidata's COVID-19 Knowledge Graph [58,61], enables the design of computational methods to explore, analyze, and integrate COVID-19-related information in decision-support systems with ease. Such semantic resources allow both manual explorations by domain experts and automatic processing by computational methods, which makes them useful in a variety of scenarios, from tracking epidemiological evolution [45] to generating public health recommendations [20,62], and supporting different informative [21] or didactic [22] needs.…”