YourDataStories: Transparency and Corruption Fighting Through Data Interlinking and Visual Exploration

  • Georgios Petasis
  • Anna Triantafillou
  • Eric Karstens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10750)

Abstract

While there are several existing infrastructures for data analytics, mainly oriented towards the analysis of tabular data, the integration of analytics with linked data is still a challenging task. There are several reasons for this lack of interoperability between linked data and analytics, including representation issues and inability to exploit the full potential of linked data. In this paper we present “YourDataStories”, an infrastructure that tries to minimise the gap between linked data (where RDF is dominant) and Web-based analytics platforms (where JSON is dominant), through the use of technologies like JSON-LD, Hydra, and a set of reusable visualisation components, which do not depend on a specific semantic model and can adapt to almost any semantic model. The presented infrastructure has been used to create a set of visualisation applications, ranging from Web visual applications (such as facet search, dashboards, and “wizard-like” interfaces allowing various kind of stakeholders to find, understand and consume the data), to mobile and social media applications that inform citizens and gather their feedback. Having as a central component the model analyser, which retrieves and analyses a semantic model (based on OWL and SKOS) from a triple store, the resulting infrastructure and applications allow users to “drill-down” on the available data and extract subsets of interest, which can be analysed through pre-configured and user-driven custom visualisations and dashboards.

Notes

Acknowledgments

This paper is supported by the project “Your Data Stories – YDS”, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 645886.

References

  1. 1.
    Brunetti, J.M., Auer, S., García, R., Klímek, J., Nečaský, M.: Formal linked data visualization model. In: Proceedings of the International Conference on Information Integration and Web-Based Applications& Services, IIWAS 2013, pp. 309:309–309:318. ACM, New York (2013)Google Scholar
  2. 2.
    W3C Recommendation: SKOS simple knowledge organization system reference (2009). https://www.w3.org/TR/skos-reference/
  3. 3.
    Baron Neto, C., Müller, K., Brümmer, M., Kontokostas, D., Hellmann, S.: LODVader: an interface to LOD visualization, analytics and discovery in real-time. In: Proceedings of the 25th International Conference Companion on World Wide Web, WWW 2016 Companion, pp. 163–166. International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland (2016)Google Scholar
  4. 4.
    Voigt, M., Tietz, V., Piccolotto, N., Meißner, K.: Attract me!: How could end-users identify interesting resources? In: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, WIMS 2013, pp. 36:1–36:12. ACM, New York (2013)Google Scholar
  5. 5.
    Voigt, M., Pietschmann, S., Meißner, K.: A semantics-based, end-user-centered information visualization process for semantic web data. In: Hussein, T., Paulheim, H., Lukosch, S., Ziegler, J., Calvary, G. (eds.) Semantic Models for Adaptive Interactive Systems, pp. 83–107. Springer, London (2013).  https://doi.org/10.1007/978-1-4471-5301-6_5 CrossRefGoogle Scholar
  6. 6.
    Bikakis, N., Skourla, M., Papastefanatos, G.: rdf:SynopsViz – a framework for hierarchical linked data visual exploration and analysis. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8798, pp. 292–297. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11955-7_37 Google Scholar
  7. 7.
    Mutlu, B., Hoefler, P., Tschinkel, G., Veas, E., Sabol, V., Stegmaier, F., Granitzer, M.: Suggesting visualisations for published data. In: 2014 International Conference on Information Visualization Theory and Applications (IVAPP), pp. 267–275, January 2014Google Scholar
  8. 8.
    Hoefler, P., Granitzer, M., Sabol, V., Lindstaedt, S.: Linked data query wizard: a tabular interface for the semantic web. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 173–177. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41242-4_19 CrossRefGoogle Scholar
  9. 9.
    Martin, M., Abicht, K., Stadler, C., Ngonga Ngomo, A.C., Soru, T., Auer, S.: CubeViz: exploration and visualization of statistical linked data. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 219–222. ACM, New York (2015)Google Scholar
  10. 10.
    The RDF Data Cube Vocabulary: SKOS simple knowledge organization system reference (2014). https://www.w3.org/TR/vocab-data-cube/
  11. 11.
    Thellmann, K., Galkin, M., Orlandi, F., Auer, S.: LinkDaViz – automatic binding of linked data to visualizations. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 147–162. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25007-6_9 CrossRefGoogle Scholar
  12. 12.
    W3C Recommendation: OWL 2 web ontology language document overview, 2nd edn. (2012). https://www.w3.org/TR/2012/REC-owl2-overview-20121211/
  13. 13.
    JSON-LD: A JSON-based serialization for linked data (2016). http://www.json-ld.org/
  14. 14.
    Hydra: Hydra W3C community group (2016). http://www.hydra-cg.com/

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Georgios Petasis
    • 1
  • Anna Triantafillou
    • 2
  • Eric Karstens
    • 3
  1. 1.Institute of Informatics and TelecommunicationsNational Centre for Scientific Research (NCSR) “Demokritos”AthensGreece
  2. 2.Innovation LabAthens Technology Center (ATC)AthensGreece
  3. 3.European Journalism Centre (EJC)MaastrichtThe Netherlands

Personalised recommendations