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search.filters.applied.f.access: openAccess × End date: 2022 × Start date: 2022 × DDC: 020 ×

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Now showing 1 - 10 of 30
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    Open-Access-Finanzierung
    (Bonn : Bundesinstitut für Berufsbildung (BIBB), 2022) Kändler, Ulrike; Wohlgemuth, Michael; Ertl, Hubert; Rödel, Bodo
    [no abstract available]
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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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    On the Impact of Temporal Representations on Metaphor Detection
    (Paris : European Language Resources Association (ELRA), 2022) Giorgio Ottolina; Matteo Palmonari; Manuel Vimercati; Mehwish Alam; Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan; Piperidis, Stelios
    State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.
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    Toward a Comparison Framework for Interactive Ontology Enrichment Methodologies
    (Aachen, Germany : RWTH Aachen, 2022) Vrolijk, Jarno; Reklos, Ioannis; Vafaie, Mahsa; Massari, Arcangelo; Mohammadi, Maryam; Rudolph, Sebastian; Fu, Bo; Lambrix, Patrick; Pesquita, Catia
    The growing demand for well-modeled ontologies in diverse application areas increases the need for intuitive interaction techniques that support human domain experts in ontology modeling and enrichment tasks, such that quality expectations are met. Beyond the correctness of the specified information, the quality of an ontology depends on its (relative) completeness, i.e., whether the ontology contains all the necessary information to draw expected inferences. On an abstract level, the Ontology Enrichment problem consists of identifying and filling the gap between information that can be logically inferred from the ontology and the information expected to be inferable by the user. To this end, numerous approaches have been described in the literature, providing methodologies from the fields of Formal Semantics and Automated Reasoning targeted at eliciting knowledge from human domain experts. These approaches vary greatly in many aspects and their applicability typically depends on the specifics of the concrete modeling scenario at hand. Toward a better understanding of the landscape of methodological possibilities, this position paper proposes a framework consisting of multiple performance dimensions along which existing and future approaches to interactive ontology enrichment can be characterized. We apply our categorization scheme to a selection of methodologies from the literature. In light of this comparison, we address the limitations of the methods and propose directions for future work.
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    Scientific publishing sanctions in response to the Russo-Ukrainian war
    (Chichester : Wiley, 2022) Nazarovets, Maryna; Teixeira da Silva, Jaime A.
    The Russian invasion of Ukraine is negatively affecting the development of the Ukrainian academy, now and in the foreseeable future. Different academic stakeholders around the world have reacted differently to this war, some imposing sanctions against Russia and/or providing aid to Ukraine. Some scientific publishers have partially or temporarily suspended sales and marketing of products and services to research organizations in Russia and Belarus. The issue of banning publication in international journals by authors from Russian institutions remains controversial and needs to be carefully considered by various stakeholders. © 2022 The Authors. Learned Publishing published by John Wiley & Sons Ltd on behalf of ALPSP
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    OER-Projekt twillo
    (Graz : Verein Forum Neue Medien in der Lehre Austria , 2022) Plank, Margret; Krause, Noreen; Beutnagel, Britta
    [No abstract available]
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    Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
    (New York,NY,United States : Association for Computing Machinery, 2022) Alam, Mehwish; Iana, Andreea; Grote, Alexander; Ludwig, Katharina; Müller, Philipp; Paulheim, Heiko; Laforest, Frédérique; Troncy, Raphael; Médini, Lionel; Herman, Ivan
    News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.
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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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    Forschungsdaten in den Naturwissenschaften: Eine urheberrechtliche Bestandsaufnahme mit ihren Implikationen für universitäres FDM
    (Heidelberg : Universitätsbibliothek Heidelberg, 2022) Hartmann, Thomas; Heuveline, Vincent; Bisheh, Nina
    Ein Schlüsselfaktor für die Zugänglichkeit, Nachnutzbarkeit und Interoperabilität von Forschungsdaten1 ist deren urheberrechtlicher Status. In den Geistes- und Sozialwissenschaften unterliegen Forschungsdaten (z. Bsp. Texte in Form von Interviews oder sonstiger Literatur) in den meisten Fällen dem Urheberschutz. Anders ist die Situation in den Naturwissenschaften. Nicht immer, aber häufig bleiben Forschungsdaten aus diesen Fächern urheberrechtsfrei. Dies begründet der Beitrag an einem typischen Beispiel aus dem Forschungsdatenzentrum für Molekulare Materialforschung (SDC MoMaF).2 Welche rechtlichen Handlungsempfehlungen sich für das Forschungsdatenmanagement (FDM) daraus ergeben, wird am Beitragsende dargestellt.