Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version)

  • Lars HelingEmail author
  • Felix Bensmann
  • Benjamin Zapilko
  • Maribel Acosta
  • York Sure-Vetter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11762)


Many research endeavors in the social sciences rely on high-quality empirical data. Survey data is often used as a foundation to investigate social behavior. The GESIS Panel is a probability-based mixed-mode panel survey in Germany providing high-quality survey and statistical data about e.g. political opinions, well-being, and other contemporary societal topics. In general, the integration and analysis of relevant data is a time-consuming process for researchers. This is due to the fact that search, discovery, and retrieval of the survey data requires accessing various data sources providing different information in different file formats. In this paper, we present our architecture for building a Knowledge Graph of the GESIS Panel data. We present the relevant heterogeneous data sources and demonstrate how we semantically lift and interlink the data in a shared RDF model. At the core of our architecture is a Knowledge Graph representing all aspects of the surveys. It is generated in a modular fashion and, therefore, our solution can be transferred to the existing infrastructure of other survey data publishers.


Knowledge Graph Survey data RDF DDI 



This work was carried out with the support of the German Research Foundation (DFG) within the project “SoRa - Sozial-Raumwissenschaftliche Forschungsdateninfrastruktur” (see footnote 17).


  1. 1.
    Bosch, T., Cyganiak, R., Gregory, A., Wackerow, J.: DDI-RDF discovery vocabulary: a metadata vocabulary for documenting research and survey data. In: LDOW (2013)Google Scholar
  2. 2.
    Bosch, T., Wackerow, J., Cyganiak, R., Zapilko, B.: Leveraging the DDI model for linked statistical data in the social, behavioural, and economic sciences, p. 10 (2012)Google Scholar
  3. 3.
    Bosnjak, M., et al.: Establishing an open probability-based mixed-mode panel of the general population in Germany: the GESIS Ppanel. Social Science Computer Review 36(1), 103–115 (2018)CrossRefGoogle Scholar
  4. 4.
    Chaves-Fraga, D., Priyatna, F., Santana-Pérez, I., Corcho, Ó.: Virtual statistics knowledge graph generation from CSV files. In: Emerging Topics in Semantic Technologies - ISWC 2018 Satellite Events (best papers from 13 of the Workshops Co-located with the ISWC 2018 Conference), pp. 235–244 (2018).
  5. 5.
    Gherghina, S., Geissel, B.: Citizens’ conceptions of democracy and political participation in Germany. In: Workshops of European Consortium for Political Research, p. 25 (2015)Google Scholar
  6. 6.
    Gottron, T., Hachenberg, C., Harth, A., Zapilko, B.: Towards a semantic data library for the social sciences, p. 13 (2011)Google Scholar
  7. 7.
    Mayer, S.J., Schultze, M.: The effects of political involvement and cross-pressures on multiple party identifications in multi-party systems - evidence from Germany. J. Elections Public Opin. Parties 29, 1–17 (2018)Google Scholar
  8. 8.
    Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)CrossRefGoogle Scholar
  9. 9.
    Schaible, J., Zapilko, B., Bosch, T., Zenk-Möltgen, W.: Linking study descriptions to the linked open data cloud. IASSIST Q. 38(4), 38 (2015)CrossRefGoogle Scholar
  10. 10.
    Vardigan, M., Heus, P., Thomas, W.: Data documentation initiative: toward a standard for the social sciences. Int. J. Digit. Curation 3(1), 107–113 (2008)CrossRefGoogle Scholar
  11. 11.
    Zapilko, B., Schaible, J., Mayr, P., Mathiak, B.: TheSoz: a SKOS representation of the thesaurus for the social sciences, p. 7 (2012)Google Scholar
  12. 12.
    Zapilko, B., Schaible, J., Wandhöfer, T., Mutschke, P.: Applying linked data technologies in the social sciences. KI -Künstliche Intelligenz 30(2), 159–162 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lars Heling
    • 1
    Email author
  • Felix Bensmann
    • 2
  • Benjamin Zapilko
    • 2
  • Maribel Acosta
    • 1
  • York Sure-Vetter
    • 1
  1. 1.Institute AIFBKarlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.GESIS - Leibniz Institute for the Social SciencesCologneGermany

Personalised recommendations