KI - Künstliche Intelligenz

, Volume 30, Issue 2, pp 189–192 | Cite as

SEPAL: Schema Enhanced Programming for Linked Data

  • Stefan ScheglmannEmail author
  • Martin Leinberger
  • Thomas Gottron
  • Steffen Staab
  • Ralf Lämmel
Research Project


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.


SPARQL Query Schematic Information SPARQL Endpoint Intensional Semantic Sepal Project 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Stefan Scheglmann
    • 1
    Email author
  • Martin Leinberger
    • 1
  • Thomas Gottron
    • 1
  • Steffen Staab
    • 1
  • Ralf Lämmel
    • 2
  1. 1.Institute for Web Science and TechnologiesUniversity of Koblenz-LandauKoblenzGermany
  2. 2.The Software Languages TeamUniversity of Koblenz-LandauKoblenzGermany

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