Machine-actionable Knowledge for Earth Science

Workshop description
Science is facing a challenge: With the increasing number of publications, it is hard to keep an overview over relevant contributions. Knowledge is still mostly shared in human-readable form of articles that leave little possibility for machine assistance, making it hard to reuse knowledge. Generative AI in the sense of Large Language Models can provide help, but is ultimately limited as the outputs are still unstructured and often imprecise.
Research Knowledge Graphs on the other hand can make scientific information truly FAIR. Semantic descriptions of research contributions make knowledge human- and machine-readable and enable completely new forms of machine assistance for scientists. Using the example of the Open Research Knowledge Graph (ORKG), this workshop teaches you how to create and use FAIR knowledge for your specific field of research. From automatically comparing the outcomes of different papers to systematic overviews, we will go through the possibilities that this new way of scholarly communications opens up. With the Reborn Articles Approach, you will learn how to produce FAIR knowledge right from the start of your project and how to integrate it into your workflow.

Timetable
09:00-09:10 – Welcome
09:10-09:30 – Introduction: Why RKGs?
09:30-10:30 – Tutorial & Task 1: Curating Content in the ORKG
10:30-10:45 – Use Cases: What can you do with the data?
10:45-11:00 – Break
11:00-12:00 – Reborn Articles: Producing FAIR knowledge from the start 12:00-12:30 – Discussion: FAIR knowledge & Earth Sciences

Date & Location: June 12th 2025, 9:00 - 12:30, online
Speaker s: Lauren Snyder (TIB Hannover), Anna-Lena Lorenz (TIB Hannover), tbh