Navigating the publication flood with machine-actionable knowledge: the Open Research Knowledge Graph (ORKG)

Science is facing a challenge: In the ever-growing flood of publications, maintaining an overview becomes increasingly hard. The underlying issue is that we rely on outdated methods of scholarly communication. Traditional articles in pdf format are written by humans for human consumption and do not allow for machine assistance in finding, comparing and linking relevant research contributions. This document-centered information flow contributes to numerous issues such as the deficiency of peer-review or the reproducibility crisis.

AI tools provide a good starting point when looking for information but ultimately do not tackle the underlying issue. The Open Research Knowledge Graph (ORKG) aims to provide a solution by making scientific information machine-actionable. In industry, knowledge graphs are already widely in use for applications that need to manage and connect large amounts of information. In science, they can serve many purposes, from providing a knowledge hub for your working group to serving as a basis for a systematic literature review.

In this workshop, you will learn how to create a machine-actionable representation of scientific knowledge in publications and use knowledge graphs for your domain. After an introduction to the topic, you will learn to create a machine-actionable representation of a paper and combine several papers into an overview. Towards the end of this workshop you will learn how to make your own results machine-actionable directly while producing them.

The following topics will be covered: