  Large Language Models (LLMs) exploit fine-tuning as a technique to adapt to
diverse goals, thanks to task-specific training data. Task specificity should
go hand in hand with domain orientation, that is, the specialization of an LLM
to accurately address the tasks of a given realm of interest. However, models
are usually fine-tuned over publicly available data or, at most, over ground
data from databases, ignoring business-level definitions and domain experience.
On the other hand, Enterprise Knowledge Graphs (EKGs) are able to capture and
augment such domain knowledge via ontological reasoning. With the goal of
combining LLM flexibility with the domain orientation of EKGs, we propose a
novel neurosymbolic architecture that leverages the power of ontological
reasoning to build task- and domain-specific corpora for LLM fine-tuning.
