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Auflistung nach Autor:in "Dietze, Stefan"

1 - 4 von 4
  • Konferenzbeitrag
    NFDI4DS Infrastructure and Services
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schimmler, Sonja; Wentzel, Bianca; Bleier, Arnim; Dietze, Stefan; Karmakar, Saurav; Mutschke, Peter; Kraft, Angelie; Taffa, Tilahun A.; Usbeck, Ricardo; Boukhers, Zeyd; Auer, Sören; Castro, Leyla J.; Ackermann, Marcel R.; Neumuth, Thomas; Schneider, Daniel; Abedjan, Ziawasch; Latif, Atif; Limani, Fidan; Abu Ahmad, Raia; Rehm, Georg; Attar Khorasani, Sima; Lieber, Matthias
    NFDI4DataScience (NFDI4DS) is a consortium founded to support researchers in all stages of the research data lifecycle in order to conduct their research in line with the FAIR principles. The infrastructure developed targets researchers from a wide range of disciplines working in the field of data science and artificial intelligence. NFDI4DS contributes to systematically understanding the needs and challenges of researchers in various disciplines regarding data science and artificial intelligence, keeping in mind ethical, legal and social aspects. The identified needs will be addressed by support structures such as educational videos and challenges. Transparency, reproducibility and FAIRness will be improved by integrating existing and newly developed services into the NFDI4DS infrastructure, and by systematically adding all digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) to the NFDI4DS research knowledge graph. This paper presents the goals of NFDI4DS, and gives an overview on what the consortium is going to contribute to the data science and artificial intelligence communities. It focuses on existing and newly developed services and their integration.
  • Konferenzbeitrag
    NFDI4DS Shared Tasks
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Abu Ahmad, Raia; Borisova, Ekaterina; Rehm, Georg; Dietze, Stefan; Kamakar, Saurav; Otto, Wolfgang; D’Souza, Jennifer; Limani, Fidan; Usbeck, Ricardo
    Shared tasks have proven to be successful in proposing innovative solutions for challenging research problems. The NFDI4DS consortium plans to host various shared tasks to tackle problems under the umbrella of scholarly information processing. We discuss three shared tasks in detail: Field of Research Classification, Software Mention Detection, and Tracking State-of-the-Art in Empirical AI. We also briefly mention other shared tasks planned to be released in the future.
  • Konferenzbeitrag
    Research Knowledge Graphs in NFDI4DS
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Karmakar, Saurav; Zloch, Matthäus; Limani, Fidan; Zapilko, Benjamin; Upadhyaya, Sharmila; D’Souza, Jennifer; Castro, Leyla J.; Rehm, Georg; Ackermann, Marcel R.; Sack, Harald; Boukhers, Zeyd; Schimmler, Sonja; Dessí, Danilo; Mutschke, Peter; Dietze, Stefan
    The ever-increasing amount of research output through scientific articles requires means to enable transparency and a better understanding of key entities of the research lifecycle, referred to as research artifacts, such as methods, software, datasets, etc. Research Knowledge Graphs (RKG) make research artifacts findable, accessible, interoperable, and reusable (FAIR) and facilitate their interpretability. In this article, we describe the role of RKGs, from their construction to the expected benefits, including an overview and a vision of their use within the German National Research Data Infrastructure (NFDI) consortium NFDI4DataScience (NFDI4DS). This paper includes insights about the existing RKGs, how to formally represent research artifacts, and how this supports better transparency and reproducibility in data science and artificial intelligence. We also discuss key challenges, such as RKG construction, and integration, and give an outlook on future work.
  • Konferenzbeitrag
    Towards standardized vectorial resource descriptors on the web
    (INFORMATIK 2010. Service Science – Neue Perspektiven für die Informatik. Band 2, 2010) Orthuber, Wolfgang; Dietze, Stefan
    Resources with quantitative properties, e.g. measurable resources or sources for feature extraction (e.g. fingerprints), play an important role, particularly in scientific areas such as Life Sciences, the medical domain and nature sciences. In this paper we propose similarity-based representation of resources using so called Vectorial Resource Descriptors (VRDs) on the Web. The VRDs are standardized data structures which build the basis of Vectorial Web Search. Every VRD contains a feature vector and a Vector Space Identifier (VSI), and further data. In contrast to conventional keyword search, which requires matching of free text, Vectorial Web Search is well defined similarity search of numeric data. Users provide a VRD, or only the searched numeric data (i.e. the feature vector, as sequence of numbers) together with the VSI. The VSI is a HTTP URI which identifies the vector space of the feature vector, and which points to a standardized Vector Space Descriptor (VSD). So the valid distance function and the meaning of every dimension (number) of the feature vector is known by the system. For quantification of similarity the (in the VSD specified) distance function of the chosen vector space is used. The smaller the distance, the greater is the similarity of a VRD, and the higher is its rank in the search result.