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From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in Belgium

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Abstract

Since the introduction of the Decision model and notation (DMN), the standard has successfully been adopted in both industry and academia. However, no clear modelling guidelines can be found regarding the development of DMN decision models. For approaching this gap, this paper discusses modelling methodologies for the DMN standard, both at the decision requirements level as well as at the decision logic level. For that purpose, we capitalise on existing guidelines in related fields, such as decision table modelling, information systems engineering, and software engineering. We adapt, expand, and restructure the guidelines to conform to DMN model development. Additionally, we provide a real-life case that was modelled using the suggested modelling strategies and consequently, we deploy the model as a government e-service for tax management. The real-life case is concerned with clarifying tax regulations for visiting performing artists in Belgium, and it was carried out in cooperation with oKo, a Belgian industry federation for the arts, and the Ministry of Culture. The decision model was built in the Avola tool and implemented as an e-service in order to demonstrate the suitability of the DMN standard for the deployment of expert system e-government services.

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Acknowledgements

We would like to thank Avola for giving us access to their tool. Furthermore, our thanks to the Belgian industry federation for the arts (oKo) and Cultuurloket (of the Ministry of Culture) for their support on the interpretation of the laws.

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Authors and Affiliations

  1. Research Centre for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium

    Faruk Hasić & Jan Vanthienen

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  1. Faruk Hasić
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  2. Jan Vanthienen
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Correspondence to Faruk Hasić.

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Hasić, F., Vanthienen, J. From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in Belgium. Knowl Inf Syst 62, 2011–2028 (2020). https://doi.org/10.1007/s10115-019-01416-4

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