You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.
Discovery features discussed in the referenced works conform to our virtue definitions, although most of them do not provide a systematic formalisation, only rather application-specific implementations. For instance, refutability and simplicity as reviewed in [1] directly correspond to our virtues. Surprise and novelty discussed in the other works can be modelled by putting emphasis on radical claims as addressed by the conservatism virtue, only using different distance metrics for each of the respective features. We believe that our approach presents a new way to formalising discovery features that is consistent with related state of the art, but is more systematic, comprehensive and extensible. In addition, we provide an actionable set of measures implemented in the context of knowledge graphs. This enables universal applicability of our research, which is not the case in most of the rather specific afore-mentioned approaches.