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Communication Papers of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 49

Potential of Found Establishment Data Linkage for the Social Sciences

DOI: http://dx.doi.org/10.15439/2026F4286

Citation: Christian Gerhards (). Potential of Found Establishment Data Linkage for the Social Sciences. In M. Bolanowski, M. Ganzha, M. Grzegorowski, L. Maciaszek, M. Paprzycki, A. Paszkiewicz, D. Ślęzak (eds), Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS). ACSIS, Vol. 49, pages 37–43.

Full text

Abstract. Social science research on establishments classically relies on representative survey data from official registers. More recently, digital found data sources that can be analysed with AI, especially geolocatable sources such as online job advertisements and OpenStreetMap (OSM), have opened up new opportunities for establishment research. However, their representativeness and possible biases are often unclear. This paper discusses the question of how the representativeness of operational data from different geospatial sources can be compared. It proposes linking these sources and discusses the practical, methodological, and data protection challenges involved, including AI-supported matching at the individual establishment level. The paper also discusses how generative AI can be used to derive key variables, such as industry classifications, from available information, enabling comparisons with register-based benchmark data. The approach is illustrated by showing how linked geolocatable establishment data from two german projects could be used: OSM-based establishment information and online job advertisement data.

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