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Leveraging Machine-Executable Descriptive Knowledge in Design Science Research – The Case of Designing Socially-Adaptive Chatbots

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Book cover Extending the Boundaries of Design Science Theory and Practice (DESRIST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11491))

Abstract

In Design Science Research (DSR) it is important to build on descriptive (Ω) and prescriptive (Λ) state-of-the-art knowledge in order to provide a solid grounding. However, existing knowledge is typically made available via scientific publications. This leads to two challenges: first, scholars have to manually extract relevant knowledge pieces from the data-wise unstructured textual nature of scientific publications. Second, different research results can interact and exclude each other, which makes an aggregation, combination, and application of extracted knowledge pieces quite complex. In this paper, we present how we addressed both issues in a DSR project that focuses on the design of socially-adaptive chatbots. Therefore, we outline a two-step approach to transform phenomena and relationships described in the Ω-knowledge base in a machine-executable form using ontologies and a knowledge base. Following this new approach, we can design a system that is able to aggregate and combine existing Ω-knowledge in the field of chatbots. Hence, our work contributes to DSR methodology by suggesting a new approach for theory-guided DSR projects that facilitates the application and sharing of state-of-the-art Ω-knowledge.

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Authors and Affiliations

  1. Institute of Information Systems and Marketing (IISM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Jasper Feine, Stefan Morana & Alexander Maedche

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  1. Jasper Feine
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  2. Stefan Morana
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  3. Alexander Maedche
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Correspondence to Jasper Feine .

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Editors and Affiliations

  1. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA

    Bengisu Tulu

  2. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA

    Soussan Djamasbi

  3. Eller College of Management, University of Arizona, Tucson, AZ, USA

    Gondy Leroy

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Feine, J., Morana, S., Maedche, A. (2019). Leveraging Machine-Executable Descriptive Knowledge in Design Science Research – The Case of Designing Socially-Adaptive Chatbots. In: Tulu, B., Djamasbi, S., Leroy, G. (eds) Extending the Boundaries of Design Science Theory and Practice. DESRIST 2019. Lecture Notes in Computer Science(), vol 11491. Springer, Cham. https://doi.org/10.1007/978-3-030-19504-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-19504-5_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-19504-5

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