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_22: Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.
As a recent survey @cite_22 shows, the applicability of existing ontology learning approaches to (semi)automated knowledge discovery is still quite limited. Many of the techniques are dependent on manually curated resources. They also introduce a lot of assumptions during the extraction process (based on, for instance, linguistic facts valid only in the context of a particular language or discourse). This limits their universal applicability. Another problem is that the more complex knowledge representation the learned ontologies use, the more restrictive they are about their meaning. This typically leads to brittleness the often inherently vague and contextual nature of the knowledge they represent. This can easily cause problems in machine-aided knowledge discovery scenarios where we typically want to represent the knowledge implied by the input data in as unbiased way as possible. Another practical limitation is that most ontology learning system do not scale very well as reported in @cite_22 .