The Open Research Knowledge Graph (ORKG)
The Open Research Knowledge Graph (ORKG) is an open infrastructure for representing and exploring scientific knowledge in a structured and machine-readable form. Instead of treating research outputs only as full-text articles, the ORKG captures the key contributions of publications (i.e. research problems, methods, datasets, and results), and organizes them in a knowledge graph. This allows scientific knowledge to be searched, compared, and reused in new ways.
Through community contributions and AI-assisted tools, researchers can describe and link research findings from publications in a structured way. This makes it possible to generate automated comparison tables, explore the state of the art for a given research question, and discover relationships between studies across disciplines.
Who is it for?
The ORKG is designed for researchers, research communities, and data curators who want to make scientific knowledge more findable, comparable, and reusable. It supports tasks such as literature reviews, meta-analyses, and the synthesis of research results by providing structured descriptions of scientific contributions. Common uses include comparing research findings, discovering new research trends, and collaborating on interdisciplinary projects.
Link to the service
Terms of use & restrictions
To contribute or edit data in the Open Research Knowledge Graph (ORKG), users only need to create an account. However, reading and accessing the information is freely available to everyone. Creating an account is simple and free, requiring only registration with an email address.
Contact
Oliver Karras, oliver.karras@tib.eu
References
publications that reference (or report on using) the service
1. Reference on Service: Vinodh Ilangovan, Sören Auer, Markus Stocker, Lars Vogt, and Sanju Tiwari: Open Research Knowledge Graph. Cuvillier Verlag, 2024, ISBN: 9783689420039, https://cuvillier.de/get/ebook/7021/9783689420039_eBook_neu_1.pdf
2. Reference on Service: Markus Stocker, Allard Oelen, Mohamad Yaser Jaradeh, Muhammad Haris, Omar Arab Oghli, Golsa Heidari, Hassan Hussein, Anna-Lena Lorenz, Salomon Kabenamualu, Kheir Eddine Farfar, Manuel Prinz, Oliver Karras, Jennifer D’Souza, Lars Vogt, and Sören Auer (2023). FAIR scientific information with the Open Research Knowledge Graph. In B. Magagna (Ed.), FAIR Connect, vol. 1, issue 1, IOS Press. https://doi.org/10.3233/fc-221513
3. Reference on Use: Oliver Karras, Jan Göpfert, Patrick Kuckertz, Tristan Pelser, and Sören Auer (2024). Organizing Scientific Knowledge From Energy System Research Using the Open Research Knowledge Graph. In: Proceedings of the 1. NFDI4Energy Conference, ArXiv, 2024, https://doi.org/10.48550/arXiv.2401.13365
4. Reference on Use: Oliver Karras, Felix Wernlein, Jil Klünder, and Sören Auer (2023). Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering. In: Proceedings of the 17th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, ACM, 2023, https://dx.doi.org/10.1109/ESEM56168.2023.10304795
5. Reference on Use: Sören Auer, Markus Stocker, Oliver Karras, Allard Oelen, Jennifer D’Souza, and Anna-Lena Lorenz: Organizing Scholarly Knowledge in the Open Research Knowledge Graph. In: 1st Conference on Research Data Infrastructure (CoRDI) – Connecting Communities, 2023, https://dx.doi.org/https://doi.org/10.52825/CoRDI.v1i.272
Miscellaneous
The new related service ORKG Ask allows researchers to find precise answers to their research questions by leveraging advanced technologies like semantic search and large language models. Simply enter your question in natural language, and ORKG Ask will provide relevant information from a vast corpus of research articles.
You can explore ORKG Ask here: https://ask.orkg.org/