Open Research Knowledge Graph: Towards Machine Actionability in Scholarly Communication

Authors : Mohamad Yaser Jaradeh, Sören Auer, Manuel Prinz, Viktor Kovtun, Gábor Kismihók, Markus Stocker

Despite improved digital access to scientific publications in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based.

The document-oriented workflows in science publication have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis.

In this article, we present first steps towards representing scholarly knowledge semantically with knowledge graphs. We expand the currently popular RDF graph-based knowledge representation formalism to capture annotations, such as provenance information and describe how to manage such knowledge in a graph data base.

We report on the results of a first experimental evaluation of the concept and its implementations with the participants of an international conference.


Unveiling Scholarly Communities over Knowledge Graphs

Authors : Sahar Vahdati, Guillermo Palma, Rahul Jyoti Nath, Christoph Lange, Sören Auer, Maria-Esther Vidal

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.