  Knowledge graphs represent the meaning of properties of real-world entities
and relationships among them in a natural way. Exploiting semantics encoded in
knowledge graphs enables the implementation of knowledge-driven tasks such as
semantic retrieval, query processing, and question answering, as well as
solutions to knowledge discovery tasks including pattern discovery and link
prediction. In this paper, we tackle the problem of knowledge discovery in
scholarly knowledge graphs, i.e., graphs that integrate scholarly data, and
present Korona, a knowledge-driven framework able to unveil scholarly
communities for the prediction of scholarly networks. Korona implements a graph
partition approach and relies on semantic similarity measures to determine
relatedness between scholarly entities. As a proof of concept, we built a
scholarly knowledge graph with data from researchers, conferences, and papers
of the Semantic Web area, and apply Korona to uncover co-authorship networks.
Results observed from our empirical evaluation suggest that exploiting
semantics in scholarly knowledge graphs enables the identification of
previously unknown relations between researchers. By extending the ontology,
these observations can be generalized to other scholarly entities, e.g.,
articles or institutions, for the prediction of other scholarly patterns, e.g.,
co-citations or academic collaboration.
