  Companies are dealing with many cognitive changes with the introduction of
the Industry 4.0 paradigm. In this constantly changing environment, knowledge
management is a key factor. Dialog systems, being able to hold a conversation
with humans, could support the knowledge management in business environment.
Although, these systems are currently hand-coded and need the intervention of a
human being in writing all the possible questions and answers, and then
planning the interactions. This process, besides being time-consuming, is not
scalable. Conversely, a dialog system, also referred to as chatbot, can be
built from scratch by simply extracting rules from technical documentation. So,
the goal of this research is designing a methodology for automatic building of
human-machine conversational system, able to interact in an industrial
environment. An initial taxonomy, containing entities expected to be found in
maintenance manuals, is used to identify the relevant sentences of a manual
provided by the company BOBST SA and applying text mining techniques, it is
automatically expanded. The final result is a taxonomy network representing the
entities and their relation, that will be used in future works for managing the
interactions of a maintenance chatbot.
