The ORKG makes scientific knowledge human- and machine-actionable and thus enables completely new ways of machine assistance. This will help researchers find relevant contributions to their field and create state-of-the-art comparisons and reviews. With the ORKG, scientists can explore knowledge in entirely new ways and share results even across different disciplines. This presentation offered an overview about the ORKG. The presentation was made on 15.7.2021 for the meeting of Lower Saxony librarian trainees.

1. Presented by: Jennifer D’Souza, Postdoc in the ORKG team
http://orkg.org | https://projects.tib.eu/orkg/ | @orkg_org
Open Research Knowledge Graph

2. ● One of the major scientific document digitalization initiatives in our
present digital age.
● The ORKG (http://orkg.org) is hosted at TIB and
● Led by TIB director Prof. (Dr.) Soeren Auer
○ Co-led by Dr. Markus Stocker
The ORKG is ...

4. ● The global scientific knowledge base would be more than a document repository
● Scientific information and knowledge would be FAIR also for machines
○ The FAIR data principles are a set of guiding principles in order to make scientific data findable,
accessible, interoperable, and reusable in the current digital ecosystem (Wilkinson et al, 2016)
● Currently
○ Findability could be better
○ Assuming OA, accessibility is OK
○ Interoperability and Reusability is non-existent
● The problem: The scholarly communications infrastructure is stuck in the last century
○ We have managed to digitize documents that used to be in print
○ While other areas have seen a transformative digitalization
What if ...

5. Information Digitization
vs.
Information Digitalization

6. Digitization of scholarly communications
http://doi.org/10.1093/eurheartj/ehw333
… almost four centuries
The digitization of paper-based documents/books was the first breakthrough for scholarly information representation in
the digital world.

7. It is the conversion of text, pictures, or sound into a digital form that can be processed by a
computer--basically, the computer knows all information about data elements and have unique
identifiers for the data elements online.
In contrast, what is digitalization?

8. Digitalization success stories
(1) Mail Order Catalogs to Online E-Commerce portals
In the past, an entire publishing
industry was dedicated to
publishing mail order catalogs.
Today, this system is entirely revolutionized with data digitalizion.
The computer has detailed information about the products. The
Amazon search engine is one successful example operating over
digitalized data.

9. Digitalization success stories
(2) Map Booklets to Satellite-based Navigation Systems
20 years ago, people published maps
of places.
Today this has changed completely to satellite-based
navigation systems. Consider the usefulness of such
digitalized data within Google Maps or Open Street
Maps applications.

11. Problem

12. ● Digital library for machine-actionable knowledge communicated in scholarly literature
● Contains structured scholarly knowledge of content beyond keywords
○ not just bibliographic metadata
● Supports multimodal interactions through human crowdsourcing, and automated text mining
techniques
● Exists at various stages of the scientific publishing lifecycle: at time of creating knowledge as well as
writing, submitting, publishing, and reading scientific information
Open Research Knowledge Graph

13. ● Project page: https://projects.tib.eu/orkg/
● API Documentation: https://www.orkg.org/orkg/doc/api/
● Software: https://gitlab.com/TIBHannover/orkg
● Twitter: https://twitter.com/orkg_org
● Skype: https://join.skype.com/giNqo7JBpvtw
● Mailing list: https://groups.google.com/forum/#!forum/orkg
Web
http://orkg.org

14. Scholarly Knowledge. Structured.
Contributions
Methods
Materials
Results

15. Scholarly Knowledge. Structured.
Every entity in the knowledge
graph can be modelled in a
fine-grained level of detail
In the ORKG storage backend, the
structured paper information is
represented as a Knowledge Graph
linking the various concepts such
as Research Problem, Methods,
Materials, and Results under each
Contribution.

16. This involves selecting key
information phrases from the
paper about the entity.
For the user, the ORKG
interface presents a flat-view
for traversing or editing the
knowledge graph.

17. Tabulated information can also
be modeled in the ORKG.

18. Examples of related initiatives to the ORKG for digitalization
of scholarly content in a Knowledge Graph
The ORKG is aimed at structuring the Contributions of Scholarly Investigations.

19. Live Demo: https://www.orkg.org/

20. What does ORKG enable?

23. ● Scholarly work can be realized as expressions other than an article
● Scholarly content can be machine-readable
○ thus practically reusable by people, for instance, generating surveys, and
○ programmatically by machines
● Nonetheless, turning the vision and prototypes into reality is very challenging
○ Requires a significant rethinking and rewiring of the current approaches and infrastructure
Conclusion: Takeaways

24. ● Jennifer D'Souza, Anett Hoppe, Arthur Brack, Mohamad Yaser Jaradeh, Sören Auer, and Ralph Ewerth (2020). The STEM-ECR Dataset: Grounding
Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. In: Proceedings of The 12th
Language Resources and Evaluation Conference, pp. 2192-2203. European Language Resources Association. https://www.aclweb.org/anthology/2020.lrec-
1.268
● Arthur Brack, Jennifer D’Souza, Anett Hoppe, Sören Auer, and Ralph Ewerth (2020). Domain-Independent Extraction of Scientific Concepts from Research
Articles. In: Jose J. et al. (eds) Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. Springer, Cham.
https://doi.org/10.1007/978-3-030-45439-5_17
● Allard Oelen, Mohamad Yaser Jaradeh, Kheir Eddine Farfar, Markus Stocker, and Sören Auer (2019). Comparing Research Contributions in a Scholarly
Knowledge Graph. In Proceedings of the Third International Workshop on Capturing Scientific Knowledge co-located with the 10th International Conference on
Knowledge Capture (K-CAP 2019), Marina Del Rey, CA, USA, November 19. http://ceur-ws.org/Vol-2526
● Mohamad Yaser Jaradeh, Allard Oelen, Kheir Eddine Farfar, Manuel Prinz, Jennifer D’Souza, Gábor Kismihók, Markus Stocker, and Sören Auer (2019). Open
Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge. In Proceedings of the 10th International Conference on
Knowledge Capture (K-CAP ’19), November 19–21, 2019, Marina Del Rey, CA, USA. ACM, New York, NY, USA, 4 pages.
https://doi.org/10.1145/3360901.3364435
● Mohamad Yaser Jaradeh, Allard Oelen, Manuel Prinz, Markus Stocker, Sören Auer (2019). Open Research Knowledge Graph: A System Walkthrough.
Lecture Notes in Computer Science, 348-351. https://doi.org/10.1007/978-3-030-30760-8_31
● Markus Stocker, Manuel Prinz, Fatemeh Rostami, and Tibor Kempf (2018). Towards research infrastructures that curate scientific information: A use case
in life sciences. In Proceedings of the 13th International Conference on Data Integration in the Life Sciences, Hannover, Germany, November 20-21.
https://doi.org/10.1007/978-3-030-06016-9_6
● Markus Stocker, Markus Fiebig, and Alex Hardisty (2018). A Missing Link from Data to Knowledge: Infrastructure that Curate the Meaning of Data. In
Proceedings of the Göttingen-CODATA RDM Symposium 2018 on the critical role of university RDM infrastructure in transforming data to knowledge, Göttingen,
Germany, March 18-20. https://conference.codata.org/2018_Goettingen_RDM/sessions/66/paper/286/
● Sören Auer, Viktor Kovtun, Manuel Prinz, Anna Kasprzik, Markus Stocker, and Maria-Esther Vidal (2018). Towards a Knowledge Graph for Science. In
Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, Novi Sad, Serbia, June 25-27.
https://doi.org/10.1145/3227609.3227689
Publications

25. Questions?
Thank you for your attention!