  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.
