Abstract
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.
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Acknowledgments
This work was carried out with the support of the German Research Foundation (DFG) within the project “SoRa - Sozial-Raumwissenschaftliche Forschungsdateninfrastruktur” (see footnote 17).
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Heling, L., Bensmann, F., Zapilko, B., Acosta, M., Sure-Vetter, Y. (2019). Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version). In: Hitzler, P., et al. The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science(), vol 11762. Springer, Cham. https://doi.org/10.1007/978-3-030-32327-1_48
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DOI: https://doi.org/10.1007/978-3-030-32327-1_48
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