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We introduce an approach to discovery informatics that uses so calledknowledge graphs as the essential representation structure. Knowledge graph isan umbrella term that subsumes various approaches to tractable representationof large volumes of loosely structured knowledge in a graph form. It has beenused primarily in the Web and Linked Open Data contexts, but is applicable toany other area dealing with knowledge representation. In the perspective of ourapproach motivated by the challenges of discovery informatics, knowledge graphscorrespond to hypotheses. We present a framework for formalising so calledhypothesis virtues within knowledge graphs. The framework is based on a classicwork in philosophy of science, and naturally progresses from mostly informativefoundational notions to actionable specifications of measures corresponding toparticular virtues. These measures can consequently be used to determinerefined sub-sets of knowledge graphs that have large relative potential formaking discoveries. We validate the proposed framework by experiments inliterature-based discovery. The experiments have demonstrated the utility ofour work and its superiority w.r.t. related approaches.

Formalising Hypothesis Virtues in Knowledge Graphs: A General Theoretical Framework and its Validation in Literature-Based Discovery Experiments