Home > Archive > 2023 > Volume 13 Number 1 (Jan. 2023) >
IJML 2023 Vol.13(1): 39-47 ISSN: 2010-3700
DOI: 10.18178/ijml.2023.13.1.1127

Conversational AI – Virtual Assistant & Chatbot at Sika Ltd.: A Case Study from the Chemical Industry

C. Marioni, M. Soriano Ramirez, M. Gaemperli, A. Kenel, and M. Krey*

Abstract—The advances in Artificial Intelligence (AI) and Conversational Agents in recent years have opened possibilities for companies and software developers using chatbots. Several frameworks have been developed to help in the development process of chatbots. One important representative of these frameworks is the Watson™ Toolset created by IBM. In this paper, we present a use case of a chatbot that assists users in creating electronic educational material as part of the internal training platform of Sika Ltd. Through this use case, we present some of the technologies available in this space including a conversational agent that can be programmed through a graphical interface, a language tone analyzer and automated translation mechanisms. Furthermore, a summary on why and how to embed the Watson Discovery services into a chatbot will be given. We show, how these technologies can be created to develop a useful assistant, show experimental results based on interactions of educators, and discuss possibilities for further development.

Index Terms—Case study, chatbot, chemical industry, IBM Watson.

Authors are with the Zurich University of Applied Sciences School of Management and Law, St. Georgenplatz 2 8401 Winterthur, Swizerland.

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Cite: C. Marioni, M. Soriano Ramirez, M. Gaemperli, A. Kenel, and M. Krey, "Conversational AI – Virtual Assistant & Chatbot at Sika Ltd.: A Case Study from the Chemical Industry," International Journal of Machine Learning vol. 13, no. 1, pp. 39-47, 2023.

Copyright @ 2023 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).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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