Research Conference on Metadata and Semantics Research

Metadata and Semantics Research pp 457-460 | Cite as

Resource Classification as the Basis for a Visualization Pipeline in LOD Scenarios

  • Oscar Peña
  • Unai Aguilera
  • Diego López-de-Ipiña
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 544)

Abstract

After more than a decade since the first steps on the Semantic Web were set, mass adoption of these technologies is still an utopic goal. Machine-readable data should leverage to provide smarter summarisations of any dataset, making them comprehensible for any user, without the need for specific knowledge. The automatic generation of coherent visual representations based on Linked Open Data could stand for mass adoption of the Semantic Web’s vision.

Our effort towards this goal is to establish a visualization pipeline, from raw semantically annotated data as input, to insightful visualizations for data analysts as output. The first steps of this pipeline need to extract the nature of the data itself through generic SPARQL queries in order to draft the structure of the data for further stages.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Auer, S., Demter, J., Martin, M., Lehmann, J.: LODStats – an extensible framework for high-performance dataset analytics. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 353–362. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  2. 2.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284(5), 28–37 (2001)CrossRefGoogle Scholar
  3. 3.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked Data - The Story So Far. International Journal on Semantic Web and Information Systems 5(3), 1–22 (2009)CrossRefGoogle Scholar
  4. 4.
    Brunetti, J.M., Auer, S., García, R., Klímek, J., Nečaský, M.: Formal linked data visualization model. In: Proceedings of International Conference on Information Integration and Web-based Applications & Services, IIWAS 2013, pp. 309–318. ACM, New York (2013)Google Scholar
  5. 5.
    Heath, T.: How Will We Interact with the Web of Data? IEEE Internet Computing 12(5), 88–91 (2008)CrossRefGoogle Scholar
  6. 6.
    Klímek, J., Helmich, J., Neíaský, M.: Application of the Linked Data Visualization Model on Real World Data from the Czech LOD Cloud (2014)Google Scholar
  7. 7.
    Langegger, A., Woss, W.: RDFStats - an extensible RDF statistics generator and library. In: 20th International Workshop on Database and Expert Systems Application, DEXA 2009, pp. 79–83, August 2009Google Scholar
  8. 8.
    Parnas, D.L., Shore, J.E., Weiss, D.: Abstract types defined as classes of variables. In: Proceedings of the 1976 Conference on Data : Abstraction, Definition and Structure, pp. 149–154. ACM, New York (1976)Google Scholar
  9. 9.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, 1996, pp. 336–343, September 1996Google Scholar
  10. 10.
    Tukey, J.W.: Exploratory Data Analysis. Pearson, Reading (1977)MATHGoogle Scholar
  11. 11.
    Yu, L.: Follow your nose: a basic semantic web agent. In: A Developer’s Guide to the Semantic Web, pp. 533–557. Springer, Heidelberg, January 2011Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Oscar Peña
    • 1
  • Unai Aguilera
    • 1
  • Diego López-de-Ipiña
    • 1
  1. 1.Deusto Institute of Technology - DeustoTechUniversity of DeustoBilbaoSpain

Personalised recommendations