Modeling, Generating, and Publishing Knowledge as Linked Data

  • Anastasia Dimou
  • Pieter Heyvaert
  • Ruben Taelman
  • Ruben Verborgh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10180)


The process of extracting, structuring, and organizing knowledge from one or multiple data sources and preparing it for the Semantic Web requires a dedicated class of systems. They enable processing large and originally heterogeneous data sources and capturing new knowledge. Offering existing data as Linked Data increases its shareability, extensibility, and reusability. However, using Linking Data as a means to represent knowledge can be easier said than done. In this tutorial, we elaborate on the importance of semantically annotating data and how existing technologies facilitate their mapping to Linked Data. We introduce [R2]RML languages to generate Linked Data derived from different heterogeneous data formats –e.g.,  DBs, XML, or JSON– and from different interfaces –e.g.,  files or Web apis. Those who are not Semantic Web experts can annotate their data with the RMLEditor, whose user interface hides all underlying Semantic Web technologies to data owners. Last, we show how to easily publish Linked Data on the Web as Triple Pattern Fragments. As a result, participants, independently of their knowledge background, can model, annotate and publish data on their own.


Linked Data generation Linked Data publishing [R2]RML Linked Data Fragments 



The described research activities were funded by Ghent University, imec, the Flanders Innovation & Entrepreneurship (AIO), the Research Foundation – Flanders (FWO), and the European Union.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anastasia Dimou
    • 1
  • Pieter Heyvaert
    • 1
  • Ruben Taelman
    • 1
  • Ruben Verborgh
    • 1
  1. 1.Ghent University – imec – IDLabGhentBelgium

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