ORKG Workshop at the first German Open Science Festival
This is what happens when 23 Open Science Enthusiasts work together to make research machine-actionable
➔This is what happens when 23 Open Science Enthusiasts work together to make research machine-actionable
➔NLP technology is all-pervasive for commonsense knowledge. There are many causes for this. Most of the internet and its data is about commonsense knowledge and world events, so NLP technology is developed over the data domain that is most easily available. But what about the scholarly domain with its rapidly growing body of knowledge produced worldwide? These are those specialized domains of knowledge in Science, Technology, Engineering, and Mathematics (STEM), all of which open up countless doors for NLP.
The opportunities are endless!
➔In 2021, we started our first curation grant program. 9 researchers from various fields of science, engineering and computer science earned a grant to push their field’s open science efforts and curate ORKG content. With over 150 Comparisons and 12 Reviews created, the program was an overwhelming success. But not only the grantees learned something: Due to the close contact to the development team, we got valuable feedback that in parts already made its way into today’s version of the ORKG and will be a basis for constant improvement.
➔How Data Science using the Open Research Knowledge Graph could drive new research
➔Ein Interview mit dem Doktoranden Vitalis Wiens über seine Doktorarbeit, seine Arbeit an der TIB und seine Publikation in der Open-Access-Zeitschrift Scientific Reports
➔In the tipping scale of activities toward the digitalization of scholarly articles, next-generation digital library frameworks such as the Open Research Knowledge Graph, targeting scholarly contributions’ highlights, are already here! We have created the NLPContributionGraph Shared Task that formalizes the building of such a scholarly contributions-focused graph over Natural Language Processing articles as an automated task. If you are eager to build a machine learner, we have the annotated scholarly contributions’ graph data for you—come join us in this endeavor!
➔The #EuVsVirus Pan-European Hackathon was organized as a full remote event coordinated over Slack channels over the weekend of April 24-26. Our team ‘TIB ORKG’ was a team of six people who met online in one of the 500 Slack channels created for the hackathon. We grouped on a common agreement: scholarly articles structured in the ORKG was a great idea to help researchers easily comprehend the articles’ content.
➔From 24 to 26 April 2020, the Scientific Data Management (SDM) group at TIB participated in the Pan-European hackathon. The SDM group and the Software and Knowledge Engineering Laboratory (SKEL) at the National Centre for Scientific Research “Demokritos” from Greece aimed at showcasing the power of integrating scientific literature and biomedical databases to discover patterns that contribute to explaining the expected results of a corona-virus related treatment.
➔New forms of knowledge exchange in research: Using a dynamic knowledge graph, various research ideas, approaches, methods and results are to be connected and presented in a machine-readable form. In this way, completely new connections of knowledge can be revealed and researchers have easier access the state-of-the-art in a certain field.
➔Neue Formen des Wissensaustausches in der Forschung: Mit einem dynamischen Wissensgraphen sollen verschiedene Forschungsideen, -ansätze, -methoden und -ergebnisse vernetzt und maschinenlesbar dargestellt werden. So können völlig neue Zusammenhänge von Wissen zutage treten und Forschende erhalten einen leichteren Zugang zum Stand der Wissenschaft.
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