  We introduce an approach to discovery informatics that uses so called
knowledge graphs as the essential representation structure. Knowledge graph is
an umbrella term that subsumes various approaches to tractable representation
of large volumes of loosely structured knowledge in a graph form. It has been
used primarily in the Web and Linked Open Data contexts, but is applicable to
any other area dealing with knowledge representation. In the perspective of our
approach motivated by the challenges of discovery informatics, knowledge graphs
correspond to hypotheses. We present a framework for formalising so called
hypothesis virtues within knowledge graphs. The framework is based on a classic
work in philosophy of science, and naturally progresses from mostly informative
foundational notions to actionable specifications of measures corresponding to
particular virtues. These measures can consequently be used to determine
refined sub-sets of knowledge graphs that have large relative potential for
making discoveries. We validate the proposed framework by experiments in
literature-based discovery. The experiments have demonstrated the utility of
our work and its superiority w.r.t. related approaches.
