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Ruben Verborgh

Modeling, Generating, and Publishing Knowledge as Linked Data

by Anastasia Dimou, Pieter Heyvaert, Ruben Taelman, and Ruben Verborgh

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 sources (databases, XML, JSON, …) from different interfaces (documents, 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.

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Published in 2017 in Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management.

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Cite this article easily using its BibTeX entry:

@inproceedings{dimou_ekaw_2016,
  title = {Modeling, Generating, and Publishing Knowledge as {Linked Data}},
  author = {Dimou, Anastasia and Heyvaert, Pieter and Taelman, Ruben and Verborgh, Ruben},
  booktitle = {Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management},
  year = 2017,
  month = may,
  volume = 10180,
  pages = {3--14},
  editor = {Ciancarini, Paolo and Poggi, Francesco and Horridge, Matthew and Zhao, Jun and Groza, Tudor and Suarez-Figueroa, Mari Carmen and d'Aquin, Mathieu and Presutti, Valentina},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  doi = {10.1007/978-3-319-58694-6_1},
}

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IEEE
A. Dimou, P. Heyvaert, R. Taelman, and R. Verborgh, “Modeling, Generating, and Publishing Knowledge as Linked Data,” in Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management, 2017, vol. 10180, pp. 3–14.
ACM
Anastasia Dimou, Pieter Heyvaert, Ruben Taelman, and Ruben Verborgh. 2017. Modeling, Generating, and Publishing Knowledge as Linked Data. In Paolo Ciancarini et al., eds. Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management. Lecture Notes in Computer Science. Springer, 3–14.
LNCS
Dimou, A., Heyvaert, P., Taelman, R., Verborgh, R.: Modeling, Generating, and Publishing Knowledge as Linked Data. In: Ciancarini, P., Poggi, F., Horridge, M., Zhao, J., Groza, T., Suarez-Figueroa, M.C., d’Aquin, M., and Presutti, V. (eds.) Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management. pp. 3–14. Springer (2017).
APA
Dimou, A., Heyvaert, P., Taelman, R., & Verborgh, R. (2017). Modeling, Generating, and Publishing Knowledge as Linked Data. In P. Ciancarini, F. Poggi, M. Horridge, J. Zhao, T. Groza, M. C. Suarez-Figueroa, … V. Presutti (Eds.), Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management (Vol. 10180, pp. 3–14). Springer.
MLA
Dimou, Anastasia et al. “Modeling, Generating, and Publishing Knowledge as Linked Data.” Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management. Ed. Paolo Ciancarini et al. Vol. 10180. Springer, 2017. 3–14. Print. Lecture Notes in Computer Science.

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