  As the number of scientific publications and preprints is growing
exponentially, several attempts have been made to navigate this complex and
increasingly detailed landscape. These have almost exclusively taken
unsupervised approaches that fail to incorporate domain knowledge and lack the
structural organisation required for intuitive interactive human exploration
and discovery. Especially in highly interdisciplinary fields, a deep
understanding of the connectedness of research works across topics is essential
for generating insights. We have developed a unique approach to data navigation
that leans on geographical visualisation and uses hierarchically structured
domain knowledge to enable end-users to explore knowledge spaces grounded in
their desired domains of interest. This can take advantage of existing
ontologies, proprietary intelligence schemata, or be directly derived from the
underlying data through hierarchical topic modelling. Our approach uses natural
language processing techniques to extract named entities from the underlying
data and normalise them against relevant domain references and navigational
structures. The knowledge is integrated by first calculating similarities
between entities based on their shared extracted feature space and then by
alignment to the navigational structures. The result is a knowledge graph that
allows for full text and semantic graph query and structured topic driven
navigation. This allows end-users to identify entities relevant to their needs
and access extensive graph analytics. The user interface facilitates graphical
interaction with the underlying knowledge graph and mimics a cartographic map
to maximise ease of use and widen adoption. We demonstrate an exemplar project
using our generalisable and scalable infrastructure for an academic biomedical
literature corpus that is grounded against hundreds of different named domain
entities.
