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M. Khobreh, F. Ansari, U. Seidenberg:
"Applying Job-Know Ontology towards Linking Workforce Experience and Labor Productivity in Smart Factory Industry 4.0";
Keynote Lecture: Proceedings of the International Conference Theory and Applications in the Knowledge Economy, Wien (invited); 2019-07-05; in: "TAKE 2019", E. Tome, F. Kragulj (ed.); (2019), 978-989-54182-0-7; 712 - 728.

Industrialization of Artificial Intelligence (AI) raises several questions, inter alia,
whether machines can gain work experience alike human workforce, whether the on-the-job
obtained experiences may enrich existing knowledge, skills and abilities (KSCs) and untimely
lead to improve its productivity. If so, human- and machine workforce initiate a new
competition in the era of intelligentization where not only AI-enhanced and smart machines
reproduce human cognitive and physical capabilities, but also they may challenge the unique
role of human as a learner. Despite economic, ethical and societal challenges,
intelligentization undergoes rapid changes in the manufacturing enterprises. This paper
explores the linkage between gaining workforce experience and labor productivity in hybrid
man-machine settings. The ultimate goal, partially addressed in this paper, is to anticipate the
learning trajectory of human and machine workforce and thus recommend the new division
of labors and innovate new processes and products in smart factories.

Ontology, Task, Learning, Labor Productivity, Division of labor, Smart Factory