Open Access-Publikationen

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https://www.weizenbaum-library.de/handle/id/23

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Auflistung Open Access-Publikationen nach Forschungsgruppen "Digitalisierung und Öffnung der Wissenschaft"

Gerade angezeigt 1 - 3 von 3
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    Editorial: Volume 2, Issue 1
    (Weizenbaum Institute, 2022) Emmer, Martin; Krasnova, Hanna; Krzywdzinski, Martin; Metzger, Axel; Schimmler, Sonja; Ulbricht, Lena
    This second issue of the Weizenbaum Journal of the Digital Society brings together four contributions that examine the role of actors and regulation in processes of digitalization from the perspective of different disciplines. The topics include the role of the Silicon Valley discourse on entrepreneurship in legitimizing a specific model of work in the IT industry, the particularities of the European platform regulation approach, the development and enforcement problems of copyright liability regulation in Germany, and the development and regulation of automation processes in the workplace.
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    Exploring Prompt Generation Utilizing Graph Search Algorithms for Ontology Matching
    (IOS Press, 2024) Sampels, Julian; Efeoglu, Sefika; Schimmler, Sonja; Salatino, Angelo; Alam, Mehwish; Ongenae, Femke; Vahdati, Sahar; Gentile, Anna-Lisa; Pellegrini, Tassilo; Jiang, Shufan
    The interoperability of domain ontologies, developed by domain experts, necessitates their alignment before attempting to match them. Within these ontologies, defined concepts often encounter an ambiguity problem stemming from the use of natural language. This interoperability issue raises the underlying ontology matching (OM) challenge. OM might be defined as the identification of correspondences or relationships between two or more entities, such as classes or properties among two or more ontologies. Rule-based ontology matching approaches, e.g., LogMap and AML have not outperformed machine learning based matchers on the Ontology Alignment Evaluation Initiative (OAEI) benchmark datasets, especially on the OAEI Conference track since 2020. Supervised machine or deep learning approaches produce the best results but require labeled training datasets. In the era of Large Language Models (LLMs), robust zero-shot prompting of LLMs can also return convincing responses. While prompt generation requires prompt template engineering by domain experts, contextual information about the concepts to be aligned can be retrieved by leveraging graph search algorithms. In this work, we explore how graph search algorithms, namely (i) Random Walk and (ii) Tree Traversal can be utilized to retrieve the contextual information to be incorporated into prompt templates. Through these algorithms, our approach refrains from considering all triples connected with a concept to be aligned in its contextual information creation. Our experiments show that including the retrieved contextual information in prompt templates improves the matcher’s performance. Additionally, our approach outperforms previous works leveraging zero-shot prompting.
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    Ronda. Real-Time Data Provision, Processing and Publication for Open Data
    (Springer International Publishing, 2021) Kirstein, Fabian; Bacher, Dario; Bohlen, Vincent; Schimmler, Sonja; Scholl, Hans Jochen; Gil-Garcia, J. Ramon; Janssen, Marijn; Kalampokis, Evangelos; Lindgren, Ida; Rodríguez Bolívar, Manuel Pedro
    The provision and dissemination of Open Data is a flourishing concept, which is highly recognized and established in the government and public administrations domains. Typically, the actual data is served as static file downloads, such as CSV or PDF, and the established software solutions for Open Data are mostly designed to manage this kind of data. However, the rising popularity of the Internet of things and smart devices in the public and private domain leads to an increase of available real-time data, like public transportation schedules, weather forecasts, or power grid data. Such timely and extensive data cannot be used to its full potential when published in a static, file-based fashion. Therefore, we designed and developed Ronda - an open source platform for gathering, processing and publishing real-time Open Data based on industry-proven and established big data and data processing tools. Our solution easily enables Open Data publishers to provide real-time interfaces for heterogeneous data sources, fostering more sophisticated and advanced Open Data use cases. We have evaluated our work through a practical application in a production environment.