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.
@cite_30: Abstract In this paper, we review AI research on computational discovery and its recent application to the discovery of new scientific knowledge. We characterize five historical stages of the scientific discovery process, which we use as an organizational framework in describing applications. We also identify five distinct steps during which developers or users can influence the behavior of a computational discovery system. Rather than criticizing such intervention, as done in the past, we recommend it as the preferred approach to using discovery software. As evidence for the advantages of such human–computer cooperation, we report seven examples of novel, computer-aided discoveries that have appeared in the scientific literature. We consider briefly the role that humans played in each case, then examine one such interaction in more detail. We close by recommending that future systems provide more explicit support for human intervention in the discovery process.
@cite_33: 
@cite_36: Abstract : We ask first whether we need a theory of creative thinking distinct from a theory of problem solving. Subject to minor qualifications, we conclude there is no such need - that we call problem solving creative when the problems solved are relatively new and difficult. Next, we summarize what has been learned about problem solving by simulating certain human prblem solving pro cesses with digital computers. Finally, we indicate some of the differences in degree that might be observed in comparing relatively creative with relatively routine problem solving. (Author)
@cite_48: Many have offered criteria for judging a design as creative. Among these criteria have been novelty, value, and surprise. We offer a unique perspective and synthesis of these three criteria with the goal of giving agents - be they artificial, human, or collectives thereof - a common model to judge the creativity of their own designs and the designs of others, and ultimately to inform computational modelling of creative design. We illustrate an AI approach to judging creativity using an example of sustainable design -- the Bloom laptop.
@cite_43: Abstract In this paper we track the development of research in empirical discovery. We focus on four machine discovery systems that share a number of features: the use of data-driven heuristics to constrain the search for numeric laws; a reliance on theoretical terms; and the recursive application of a few general discovery methods. We examine each system in light of the innovations it introduced over its predecessors, providing some insight into the conceptual progress that has occurred in machine discovery. Finally, we reexamine this research from the perspectives of the history and philosophy of science.
Research of ways how discoveries can be automated or facilitated by machines dates back to the dawn of the digital computer era. The work @cite_36 provides a comprehensive analysis of the discovery process operationalised as creative problem solving. It reviews several classic machine discovery systems and the heuristics used by them, and also mentions several properties of worthy discoveries like novelty and value. A more recent related work @cite_33 reviews the major approaches to studying the process of scientific discovery, provides another survey of automated discovery systems and analyses additional features of relevant discoveries, such as surprise. The works @cite_43 @cite_30 review still more machine discovery systems and heuristics, and identify features like refutability and simplicity as essential to discoveries. One of the most recent and relevant works from this area is @cite_48 . It builds on @cite_36 @cite_33 and introduces formalisations of several discovery features. In particular, it models novelty and value using metric spaces, and surprise using Bayesian probabilities.