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Author: Auer, Sören × Author: Stocker, Markus × DDC: 004 × Version: acceptedVersion ×

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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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    Requirements Analysis for an Open Research Knowledge Graph
    (Berlin ; Heidelberg : Springer, 2020) Brack, Arthur; Hoppe, Anett; Stocker, Markus; Auer, Sören; Ewerth, Ralph; Hall, Mark; Merčun, Tanja; Risse, Thomas; Duchateau, Fabien
    Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KGs) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective by presenting a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications and outline possible solutions.