Open Research Knowledge Graph: Towards Machine Actionability in Scholarly Communication

Authors: Mohamad Yaser Jaradeh, Allard Oelen, Manuel Prinz, Sören Auer, Viktor Kovtun, Gábor Kismihók, Jennifer D’Souza, Markus Stocker

Abstract:Despite improved findability of and access to scientific knowledge in recent decades, scholarly communication continues to be document-based. Scientific knowledge remains locked in representations that are inadequate for machine processing. In this article, we present initial steps towards next generation digital libraries and infrastructures that acquire, curate, publish and process scholarly knowledge semantically, in machine readable form leveraging knowledge graphs. The primary contribution of this work is to present and discuss early developments of a system designed to crowdsource machine readable descriptions of research contributions published in scholarly articles and a knowledge graph infrastructure for description storage and access. We report on the results of a first experimental evaluation of the concept and its implementation with the participants of a recent international conference. The results suggest that users find such a system useful, and the possibilities it could enable intriguing.

Citation:Jaradeh, M.Y., Auer, S., Prinz, M., Kovtun, V., Kismihók, G., & Stocker, M. (2019). Open Research Knowledge Graph: Towards Machine Actionability in Scholarly Communication. arXiv:1901.10816v2.

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Source: ArXiv

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