Abstract
Conceptual modeling applies abstraction to reduce the complexity of a system under study to produce a human interpretable, formalized representation (i.e., a conceptual model). Such models enable understanding and communication among humans and processing by machines. Artificial Intelligence (AI) algorithms are also applied to complex realities (regularly represented by vast amounts of data) to identify patterns or classify entities in the data automatically. However, AI differs from conceptual modeling because the results are often neither comprehensible nor explainable nor reproducible. AI systems often act as a black box; not even their developers can explain their behavior. The uptake of AI is recognizable across all disciplines and domains, both in academia and industry. The enterprise engineering field is no exception to this trend. In this paper, which is based on a keynote delivered at EEWC 2021, we present selected recent contributions at the intersection of conceptual modeling and AI, thereby shedding light on challenges and opportunities for enterprise engineering.
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Acknowledgements
The author wants to thank the organization committee of EEWC 2021 and the EEWC steering committee for the invitation to deliver a keynote. Moreover, the author wants to thank Syed Juned Ali for supporting the preparation of the keynote and this paper.
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Bork, D. (2022). Conceptual Modeling and Artificial Intelligence: Challenges and Opportunities for Enterprise Engineering. In: Aveiro, D., Proper, H.A., Guerreiro, S., de Vries, M. (eds) Advances in Enterprise Engineering XV. EEWC 2021. Lecture Notes in Business Information Processing, vol 441. Springer, Cham. https://doi.org/10.1007/978-3-031-11520-2_1
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