Covid-on-the-Web: Exploring the COVID-19 scientific literature through visualization of linked data from entity and argument mining

Abstract

The unprecedented mobilization of scientists, consequent of the COVID-19 pandemics, has generated an enormous number of scholarly articles that is impossible for a human being to keep track and explore without appropriate tool support. In this context, we created the Covid-on-the-Web project, which aims to assist the access, querying, and sense making of COVID-19 related literature by combining efforts from semantic web, natural language processing, and visualization fields. Particularly, in this paper, we present (i) an RDF dataset, a linked version of the “COVID-19 Open Research Dataset” (CORD-19), enriched via entity linking and argument mining, and (ii) the “Linked Data Visualizer” (LDViz), 28 which assists the querying and visual exploration of the referred dataset. The LDViz tool assists the exploration of different views of the data by combining a querying management interface, which enables the definition of meaningful subsets of data through SPARQL queries, and a visualization interface based on a set of six visualization techniques integrated in a chained visualization concept, which also supports the tracking of provenance information. We demonstrate the potential of our approach to assist biomedical researchers in solving domain-related tasks, as well as to perform exploratory analyses through use case scenarios.

RuArg-2022: Argument Mining Evaluation

“…First, they identify relevant tweets for governmental measures and if relevant, detect what stance is expressed. (Menin et al, 2022) create a linked data version of the CORD-19 data set and enriched it via entity linking and argument mining.…”