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End date: 2024 × Start date: 2024 × Start date: 1984 × DDC: 004 × DDC: 020 × Type: Text ×

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    TinyGenius: Intertwining natural language processing with microtask crowdsourcing for scholarly knowledge graph creation
    (New York,NY,United States : Association for Computing Machinery, 2022) Oelen, Allard; Stocker, Markus; Auer, Sören; Aizawa, Akiko
    As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.
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    Knowledge Graphs - Working Group Charter (NFDI section-metadata) (1.2)
    (Genève : CERN, 2023) Stocker, Markus; Rossenova, Lozana; Shigapov, Renat; Betancort, Noemi; Dietze, Stefan; Murphy, Bridget; Bölling, Christian; Schubotz, Moritz; Koepler, Oliver
    Knowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains.
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    Open Research Knowledge Graph
    (Goettingen: Cuvillier Verlag, 2024-05-07) Auer, Sören; Ilangovan, Vinodh; Stocker, Markus; Tiwari, Sanju; Vogt, Lars; Bernard-Verdier, Maud; D'Souza, Jennifer; Fadel , Kamel; Farfar, Kheir Eddine; Göpfert , Jan; Haris , Muhammad; Heger, Tina; Hussein, Hassan; Jaradeh, Yaser; Jeschke, Jonathan M.; Jiomekong , Azanzi; Kabongo, Salomon; Karras, Oliver; Kuckertz, Patrick; Kullamann, Felix; Martin, Emily A.; Oelen, Allard; Perez-Alvarez, Ricardo; Prinz, Manuel; Snyder, Lauren D.; Stolten, Detlef; Weinand, Jann M.
    As we mark the fifth anniversary of the alpha release of the Open Research Knowledge Graph (ORKG), it is both timely and exhilarating to celebrate the significant strides made in this pioneering project. We designed this book as a tribute to the evolution and achievements of the ORKG and as a practical guide encapsulating its essence in a form that resonates with both the general reader and the specialist. The ORKG has opened a new era in the way scholarly knowledge is curated, managed, and disseminated. By transforming vast arrays of unstructured narrative text into structured, machine-processable knowledge, the ORKG has emerged as an essential service with sophisticated functionalities. Over the past five years, our team has developed the ORKG into a vibrant platform that enhances the accessibility and visibility of scientific research. This book serves as a non-technical guide and a comprehensive reference for new and existing users that outlines the ORKG’s approach, technologies, and its role in revolutionizing scholarly communication. By elucidating how the ORKG facilitates the collection, enhancement, and sharing of knowledge, we invite readers to appreciate the value and potential of this groundbreaking digital tool presented in a tangible form. Looking ahead, we are thrilled to announce the upcoming unveiling of promising new features and tools at the fifth-year celebration of the ORKG’s alpha release. These innovations are set to redefine the boundaries of machine assistance enabled by research knowledge graphs. Among these enhancements, you can expect more intuitive interfaces that simplify the user experience, and enhanced machine learning models that improve the automation and accuracy of data curation. We also included a glossary tailored to clarifying key terms and concepts associated with the ORKG to ensure that all readers, regardless of their technical background, can fully engage with and understand the content presented. This book transcends the boundaries of a typical technical report. We crafted this as an inspiration for future applications, a testament to the ongoing evolution in scholarly communication that invites further collaboration and innovation. Let this book serve as both your guide and invitation to explore the ORKG as it continues to grow and shape the landscape of scientific inquiry and communication.