Abstract—Knowledge and design captured in the heads of people is a starting point for the abductive design process in which design thinking and its tools support the ideation and externalization of design prescriptions. Turning these design prescriptions into innovative solutions implies to further decompose design thinking artifacts while enriching them with semantics, as this enables a bridging opportunity to conceptual models representing actual enterprise and operational contexts. Realizing the bridging opportunity requires to intertwine digital twins of design thinking artifacts with the semantic expressiveness of conceptual modelling methods. The problem is that this requires a considerable knowledge engineering effort. To ease the problem, machine learning concepts are introduced into the Design2Model approach employed in OMiLAB. Furthermore, a corresponding tool is realized. As a result, the paper at hand reports on a novel application case in which machine learning and knowledge engineering is combined to realize – in an effective way – the bridging opportunity between design thinking and conceptual modelling. In particular, the case covers a component of the Design2Model tool responsible for digitalizing, semantifying, and processing of Post-it Notes from a design thinking workshop and their representations in diagrammatic conceptual models. The report focuses on technologies used and their qualitative artificial ex-post evaluation in a laboratory environment.
Index Terms—Handwritten text recognition, semantic image segmentation, semantic interoperability using RDF.
M. Walch and D. Karagiannis are with the Department of Knowledge Engineering, Faculty of Computer Science, University of Vienna, Vienna, Austria (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Michael Walch and Dimitris Karagiannis, "Design Thinking and Knowledge Engineering: A Machine Learning Case," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 765-770, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).