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Auflistung nach Autor:in "Scheglmann, Stefan"

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  • Zeitschriftenartikel
    SEPAL: Schema Enhanced Programming for Linked Data
    (KI - Künstliche Intelligenz: Vol. 30, No. 2, 2016) Scheglmann, Stefan; Leinberger, Martin; Gottron, Thomas; Staab, Steffen; Lämmel, Ralf
    The linked data cloud provides a large collection of interlinked data and is supposed to be seen as one, big data source. However, when developing applications against this data source, it becomes apparent that different challenges arise in the various steps of programming. Among these are the selection and conceptualization of data as well as the process of actually accessing the data. The schema enhanced programming for linked data (SEPAL) project provides a new approach for integrating linked data sources when developing semantic web applications. It does this by crawling the LOD cloud, analyzing the extracted data and providing this information to a developer during development through a framework that extends the programing language. In this paper, we will motivate the necessity for a project like SEPAL and explain the core components of the project.