Exploration and Discovery of the COVID-19 Literature through Semantic Visualization
This work addresses the challenge of navigating and extracting insights from complex scientific literature for researchers, but it appears incremental as it builds on existing semantic visualization methods.
The authors tackled the problem of exploring and discovering insights in large datasets of complex networks, specifically applying semantic visualization techniques to the COVID-19 literature (CORD-19 dataset) to enable novel inferences that might otherwise go unnoticed.
We are developing semantic visualization techniques in order to enhance exploration and enable discovery over large datasets of complex networks of relations. Semantic visualization is a method of enabling exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves (i) NLP to extract named entities, relations and knowledge graphs from the original data; (ii) indexing the output and creating representations for all relevant entities and relations that can be visualized in many different ways, e.g., as tag clouds, heat maps, graphs, etc.; (iii) applying parameter reduction operations to the extracted relations, creating "relation containers", or functional entities that can also be visualized using the same methods, allowing the visualization of multiple relations, partial pathways, and exploration across multiple dimensions. Our hope is that this will enable the discovery of novel inferences over relations in complex data that otherwise would go unnoticed. We have applied this to analysis of the recently released CORD-19 dataset.