  Here we present a holistic approach for data exploration on dense knowledge
graphs as a novel approach with a proof-of-concept in biomedical research.
Knowledge graphs are increasingly becoming a vital factor in knowledge mining
and discovery as they connect data using technologies from the semantic web. In
this paper we extend a basic knowledge graph extracted from biomedical
literature by context data like named entities and relations obtained by text
mining and other linked data sources like ontologies and databases. We will
present an overview about this novel network. The aim of this work was to
extend this current knowledge with approaches from graph theory. This method
will build the foundation for quality control, validation of hypothesis,
detection of missing data and time series analysis of biomedical knowledge in
general. In this context we tried to apply multiple-valued decision diagrams to
these questions. In addition this knowledge representation of linked data can
be used as FAIR approach to answer semantic questions. This paper sheds new
lights on dense and very large knowledge graphs and the importance of a
graph-theoretic understanding of these networks.
