Abstract
One aspect of knowledge management is concerned with the alignment between what is captured in the heads of people and what is encoded by technology. The alignment of knowledge is necessary as humans possess an efficient ability to design innovation based on business insights, while technological systems are able to operating efficiently in different environments. To support knowledge management, this study presents systematic foundations covering a knowledge-based conceptual modelling approach. On a systematic level, three procedures are presented to facilitate the alignment of knowledge between people and technology: the decomposition of concepts from design thinking in conceptual models, the abstraction of capabilities from intelligent environments in conceptual models, and the (semi-) automated, intelligent transformation of conceptual models. Furthermore, the architecture of ICT infrastructure supporting the three procedures is addressed. These systematic foundations are integrated in the OMiLAB ecosystem and instantiated in two projects. The first project revolves around PRINTEPS, which is a framework to develop practical Artificial Intelligence. The second project revolves around s*IoT, which is a unifying semantic-aware modelling environment for the Internet of Things. Additionally, two concrete cases are presented for both project. Due to employing common systematic foundations, transfer and reuse among the two projects is facilitated.
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Acknowledgement
A part of this study was supported by the project of “A Framework PRINTEPS to Develop Practical Artificial Intelligence,” (JPMJCR14E3) the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST).
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Walch, M., Morita, T., Karagiannis, D., Yamaguchi, T. (2019). A Knowledge-Based Conceptual Modelling Approach to Bridge Design Thinking and Intelligent Environments. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_47
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DOI: https://doi.org/10.1007/978-3-030-29551-6_47
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