Facilitator
Anna-Lena Lorenz (@AnnaLorenz)
Abstract
Science is facing a challenge: In the ever growing flood of publications, maintaining an overview becomes increasingly hard. Many researchers wish for AI tools to aid them in the process of finding and analyzing literature. But purely relying on Large Language Models can lead to misinformation or hallucination.
The Open Research Knowledge Graph (ORKG) aims to provide a solution by combining the power and high useability of LLMs with a human-in-the-loop approach for in-depth content curation.
Our novel tool, ORKG Ask, answers research questions based on almost 80 million open access articles. Researchers are given an overview over the key insights from the relevant publications as well as used methods, limitations and conclusions. Thereby, the content is fully customizable, so that researchers can extract additional details on demand or filter on the metadata.
But it does not end with a human-readable overview. In the ORKG, we structure the contents of scholarly publications in a knowledge graph, making them human- and machine-actionable. At its core, the ORKG serves as a central hub for organizing scientific information from scholarly publications, including papers, datasets and software. The contents are curated by researchers in a crowd-based approach. While ORKG Ask serves as a good entry point to a topic, this knowledge graph represents knowledge in a structured form and give more detail. All content is completely controlled by domain experts and can be collaboratively edited, reviewed and discussed.
In this workshop we will give a brief introduction to ORKG’s vision, followed by instructions on using the platform and a hands-on-session on curating content as well as a follow up discussion on LLM based methods for scholarly communication.
To register your participation in this workshop click on the “Going” button above. You will then receive an email notification as soon as facilitators post an update. Watch out to not register for two parallel workshops.