Abstract
Despite improved digital access to scholarly literature in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. Scholarly knowledge remains locked in representations that are inadequate for machine processing. The Open Research Knowledge Graph (ORKG) is an infrastructure for representing, curating and exploring scholarly knowledge in a machine actionable manner. We demonstrate the core functionality of ORKG for representing research contributions published in scholarly articles. A video of the demonstration [7] and the system (https://labs.tib.eu/orkg/ ) are available online.
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CITATION STYLE
Jaradeh, M. Y., Oelen, A., Prinz, M., Stocker, M., & Auer, S. (2019). Open Research Knowledge Graph: A System Walkthrough. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11799 LNCS, pp. 348–351). Springer Verlag. https://doi.org/10.1007/978-3-030-30760-8_31
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