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End date: 2024 × Start date: 2022 × End date: 2023 × Start date: 2020 × DDC: 020 ×

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Now showing 1 - 10 of 91
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    Handreichung Urheberrecht und Datenschutz
    (Genève : CERN, 2023) Blumtritt, Ute; Euler, Ellen; Fadeevy, Yuliya; Pohle, Jörg; Rack, Fabian; Wrzesinski, Marcel
    Die vorliegende Handreichung adressiert wissenschaftsgeleitete Zeitschriften sowie herausgebende Einrichtungen. Sie sollen in die Lage versetzt werden, erste urheberrechtliche wie datenschutzrechtliche Fragen zu beantworten und dabei Qualitätsstandards einzuhalten. Dieser Text ersetzt keine Rechtsberatung, sondern bietet grundsätzliche Informationen, gibt Empfehlungen zum Weiterlesen für klassische Fragestellungen und verweist auf gelungene Beispiele im weiteren Feld des wissenschaftsgeleiteten Publizierens.
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    Handreichung Technik und Infastrukturen
    (Genève : CERN, 2023) Eichler, Frederik; Eppelin, Anita; Kampkaspar, Dario; Schrader, Antonia C.; Söllner, Konstanze; Vierkant, Paul; Withanage, Dulip; Wrzesinski, Marcel
    In der vorliegenden Handreichung stellen wir unterschiedliche technische Ressourcen vor, die redaktionelle Arbeiten unterstützen können. Dabei empfiehlt es sich, Software und Systeme zu nutzen, die den Wandel hin zu einer offenen, niederschwelligen und nachhaltigen Wissenschaftskultur fördern. Hierzu zählt in erster Linie die Verwendung von Open-Source-Software. Unsere Empfehlungen haben dabei eine begrenzte Reichweite: Serviceanbieter, Software und Projekte sind zu einem späteren Zeitpunkt ggf. nicht mehr verfügbar. Auch sind gerade die Infrastruktureinrichtungen in das föderale Wissenschaftssystem integriert, was sie bestimmten Unwägbarkeiten aussetzt.
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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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    An OER Recommender System Supporting Accessibility Requirements
    (New York : Association for Computing Machinery, 2020) Elias, Mirette; Tavakoli, Mohammadreza; Lohmann, Steffen; Kismihok, Gabor; Auer, Sören; Gurreiro, Tiago; Nicolau, Hugo; Moffatt, Karyn
    Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.
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    Modelling Archival Hierarchies in Practice: Key Aspects and Lessons Learned
    (Aachen, Germany : RWTH Aachen, 2021) Vafaie, Mahsa; Bruns, Oleksandra; Pilz, Nastasja; Dessì, Danilo; Sack, Harald; Sumikawa, Yasunobu; Ikejiri, Ryohei; Doucet, Antoine; Pfanzelter, Eva; Hasanuzzaman, Mohammed; Dias, Gaël; Milligan, Ian; Jatowt, Adam
    An increasing number of archival institutions aim to provide public access to historical documents. Ontologies have been designed, developed and utilised to model the archival description of historical documents and to enable interoperability between different information sources. However, due to the heterogeneous nature of archives and archival systems, current ontologies for the representation of archival content do not always cover all existing structural organisation forms equallywell. After briefly contextualising the heterogeneity in the hierarchical structure of German archives, this paper describes and evaluates differences between two archival ontologies, ArDO and RiC-O, and their approaches to modelling hierarchy levels and archive dynamics.
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    DDB-KG: The German Bibliographic Heritage in a Knowledge Graph
    (Aachen, Germany : RWTH Aachen, 2021) Tan, Mary Ann; Tietz, Tabea; Bruns, Oleksandra; Oppenlaender, Jonas; Dessì, Danilo; Harald, Sack; Sumikawa, Yasunobu; Ikejiri, Ryohei; Doucet, Antoine; Pfanzelter, Eva; Hasanuzzaman, Mohammed; Dias, Gaël; Milligan, Ian; Jatowt, Adam
    Under the German government’s initiative “NEUSTART Kultur”, the German Digital Library or Deutsche Digitale Bibliothek (DDB) is undergoing improvements to enhance user-experience. As an initial step, emphasis is placed on creating a knowledge graph from the bibliographic record collection of the DDB. This paper discusses the challenges facing the DDB in terms of retrieval and the solutions in addressing them. In particular, limitations of the current data model or ontology to represent bibliographic metadata is analyzed through concrete examples. This study presents the complete ontological mapping from DDB-Europeana Data Model (DDB-EDM) to FaBiO, and a prototype of the DDB-KG made available as a SPARQL endpoint. The suitabiliy of the target ontology is demonstrated with SPARQL queries formulated from competency questions.
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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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    A Data Model for Linked Stage Graph and the Historical Performing Arts Domain
    (Aachen, Germany : RWTH Aachen, 2023) Tietz, Tabea; Bruns, Oleksandra; Sack, Harald; Bikakis, Antonis; Ferrario, Roberta; Jean, Stéphane; Markhoff, Béatrice; Mosca, Alessandro; Nicolosi Asmundo, Marianna
    The performing arts are complex, dynamic and embedded into societal and political systems. Providing means to research historical performing arts data is therefore crucial for understanding our history and culture. However, currently no commonly accepted ontology for historical performing arts data exists. On the example of the Linked Stage Graph, this position paper presents the ongoing process of creating an application-driven and efficient data model by leveraging and building upon existing standards and ontologies like CIDOC-CRM, FRBR, and FRBRoo.
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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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    Designing Intelligent Systems for Online Education: Open Challenges and Future Directions
    (Aachen, Germany : RWTH Aachen, 2021) Dessì, Danilo; Käser, Tanja; Marras, Mirko; Popescu, Elvira; Sack, Harald; Dessì, Danilo; Käser, Tanja; Marras, Mirko; Popescu, Elvira; Sack, Harald
    The design and delivering of platforms for online education is fostering increasingly intense research. Scaling up education online brings new emerging needs related with hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely, as examples. However, with the impressive progress of the data mining and machine learning fields, combined with the large amounts of learning-related data and high-performance computing, it has been possible to gain a deeper understanding of the nature of learning and teaching online. Methods at the analytical and algorithmic levels are constantly being developed and hybrid approaches are receiving an increasing attention. Recent methods are analyzing not only the online traces left by students a posteriori, but also the extent to which this data can be turned into actionable insights and models, to support the above needs in a computationally efficient, adaptive and timely way. In this paper, we present relevant open challenges lying at the intersection between the machine learning and educational communities, that need to be addressed to further develop the field of intelligent systems for online education. Several areas of research in this field are identified, such as data availability and sharing, time-wise and multi-modal data modelling, generalizability, fairness, explainability, interpretability, privacy, and ethics behind models delivered for supporting education. Practical challenges and recommendations for possible research directions are provided for each of them, paving the way for future advances in this field.