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Author: Stocker, Markus × Version: publishedVersion × Subject: Konferenzschrift ×

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    Building Scholarly Knowledge Bases with Crowdsourcing and Text Mining
    (Aachen : RWTH, 2020) Stocker, Markus; Zhang, Chengzhi; Mayr, Philipp; Lu, Wei; Zhang, Yi
    For centuries, scholarly knowledge has been buried in documents. While articles are great to convey the story of scientific work to peers, they make it hard for machines to process scholarly knowledge. The recent proliferation of the scholarly literature and the increasing inability of researchers to digest, reproduce, reuse its content are constant reminders that we urgently need a transformative digitalization of the scholarly literature. Building on the Open Research Knowledge Graph (http://orkg.org) as a concrete research infrastructure, in this talk we present how using crowdsourcing and text mining humans and machines can collaboratively build scholarly knowledge bases, i.e. systems that acquire, curate and publish data, information and knowledge published in the scholarly literature in structured and semantic form. We discuss some key challenges that human and technical infrastructures face as well as the possibilities scholarly knowledge bases enable.
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    Crowdsourcing Scholarly Discourse Annotations
    (New York, NY : ACM, 2021) Oelen, Allard; Stocker, Markus; Auer, Sören
    The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.