Improving Access to Scientific Literature with Knowledge Graphs

Abstract

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.

Researcher or Crowd Member? Why not both! The Open Research Knowledge Graph for Applying and Communicating CrowdRE Research

“…1). On the one hand, we analyzed the documentation from the project page 3 , the wiki 4 , GitLab 5 , and related publications [1]- [3], [19], [32]- [34]. On the other hand, we used the ORKG as crowd members to acquire, curate, and publish scholarly knowledge about CrowdRE research [35], [36].…”

“…Historically, research results were published in collections of printed works, such as journals [1]. Studying the literature on a particular topic required hours or days of browsing through a library's collection.…”

“…The next step in the digital transformation of scholarly knowledge requires a more flexible, fine-grained, semantic, and context-sensitive representation that allows humans to quickly compare research results, and machines to process them. This representation can hardly be created automatically, but requires domain experts [1] and an infrastructure for acquiring, curating, publishing, and processing scholarly knowledge [3].…”

“…The ORKG is a platform that uses crowdsourcing to acquire and curate scholarly knowledge. The project explores how scholarly knowledge can be acquired along the research lifecycle, relying on the manual acquisition and curation of scholarly knowledge through crowdsourced contributions from experts [1], [3], [13]. Researchers from various research fields form a crowd that acquires, curates, publishes, and processes scholarly knowledge.…”

“…Researchers from various research fields form a crowd that acquires, curates, publishes, and processes scholarly knowledge. Based on the publicly available beta version of the ORKG 1 , the ORKG project team has two longterm goals. First, they aim to integrate more strategies for crowdsourcing to enable crowd members to contribute their research results to the ORKG in a more flexible and lightweight manner [13, p. 9].…”