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Cross-Administration Comparative Analysis of Open Fiscal Data

: Musyaffa, Fathoni A.; Lehmann, Jens; Jabeen, Hajira


Charalabidis, Y. ; Association for Computing Machinery -ACM-:
13th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2020. Proceedings : 23-25 September 2020, Online
New York: ACM, 2020
ISBN: 978-1-4503-7674-7
International Conference on Theory and Practice of Electronic Governance (ICEGOV) <13, 2020, Online>
European Commission EC
H2020; 809965; LAMBDA
Learning, Applying, Multiplying Big Data Analytics
Fraunhofer IAIS ()
Semantic Web; budget and spending; knowledge graph; linked open data; comparative analysis

To improve governance accountability, public administrations are increasingly publishing their open data, which includes budget and spending data. Analyzing these datasets requires both domain and technical expertise. In civil communities, these technical and domain expertise are often not available. Hence, despite the increasing size of the open fiscal datasets being published, the level of analytics done on top of these datasets is still limited. There is a plethora of tools and ontologies for open fiscal data e.g., transformation, linking, multilingual integration, and classification. These existing technologies enable the development of a pipeline that could be used for comparative analysis of open fiscal data. In this paper, we demonstrate the comparative analysis over linked open fiscal data. Open fiscal data are cleaned, analyzed, transformed (i.e., semantically lifted), and have their related concept labels connected across different public administrations so budget/spending items from related concepts can be queried. Additionally, the information on linked open data (e.g., DBpedia) has been used to provide additional context for the analysis. We provide a proof-of-concept and demonstrate that such a cross-comparison is possible using the existing tools.