Improving Access to Scientific Literature with Knowledge Graphs

• Published in: Bibliothek Forschung und Praxis Vol. 44; no. 3; pp. 516 - 529
• Main Authors: Auer, Sören, Oelen, Allard, Haris, Muhammad, Stocker, Markus, D’Souza, Jennifer, Farfar, Kheir Eddine, Vogt, Lars, Prinz, Manuel, Wiens, Vitalis, Jaradeh, Mohamad Yaser
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 01.12.2020; Walter de Gruyter GmbH
• Subjects: crowdsourcing; Graph representations; Knowledge; knowledge graph; Sacherschließung; semantic web; Semantics; Subject classification; text mining; Wissensgraph
• ISSN: 0341-4183; 1865-7648
• DOI: 10.1515/bfp-2020-2042

The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.