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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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    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.
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    B!SON: A Tool for Open Access Journal Recommendation
    (Heidelberg : Springer, 2022) Entrup, Elias; Eppelin, Anita; Ewerth, Ralph; Hartwig, Josephine; Tullney, Marco; Wohlgemuth, Michael; Hoppe, Anett; Nugent, Ronan
    Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project.
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    Rechtliche Fragen bei der Nutzung von Abbildungen aus Open-Access-Publikationen
    (Heidelberg : Universitätsbibliothek Heidelberg, 2022) Sohmen, Lucia; Rack, Fabian; Heuveline, Vincent; Bisheh, Nina
    Die zunehmende Verfügbarkeit von Forschungsdaten eröffnet Forschenden neue Möglichkeiten, mit von Dritten erstellten Forschungsdaten zu arbeiten. Dieser Beitrag befasst sich mit der Frage, welche rechtlichen Rahmenbedingungen gelten, wenn diese nachgenutzten Forschungsdaten öffentlich verfügbar gemacht werden sollen. Im Speziellen geht der Artikel dabei auf Bildersuchmaschinen und das Veröffentlichen von Bildkorpora ein. Dabei wird dargestellt, dass es bei der öffentlichen Zugänglichmachung von unübersichtlichen Bildmengen keine hundertprozentige Sicherheit geben kann. Durch bestimmte Abwägungen und technische Mittel kann sich dieser aber angenähert werden.
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    Causal Relationship over Knowledge Graphs
    (2022) Huang, Hao; Al Hasan, Mohammad; Xiong, Li
    Causality has been discussed for centuries, and the theory of causal inference over tabular data has been broadly studied and utilized in multiple disciplines. However, only a few works attempt to infer the causality while exploiting the meaning of the data represented in a data structure like knowledge graph. These works offer a glance at the possibilities of causal inference over knowledge graphs, but do not yet consider the metadata, e.g., cardinalities, class subsumption and overlap, and integrity constraints. We propose CareKG, a new formalism to express causal relationships among concepts, i.e., classes and relations, and enable causal queries over knowledge graphs using semantics of metadata. We empirically evaluate the expressiveness of CareKG in a synthetic knowledge graph concerning cardinalities, class subsumption and overlap, integrity constraints. Our initial results indicate that CareKG can represent and measure causal relations with some semantics which are uncovered by state-of-the-art approaches.