  Knowledge acquisition is the essential first step of any Knowledge Graph (KG)
application. This knowledge can be extracted from a given corpus (KG generation
process) or specified from an existing KG (KG specification process). Focusing
on domain specific solutions, knowledge acquisition is a labor intensive task
usually orchestrated and supervised by subject matter experts. Specifically,
the domain of interest is usually manually defined and then the needed
generation or extraction tools are utilized to produce the KG. Herein, we
propose a supervised machine learning method, based on Transformers, for domain
definition of a corpus. We argue why such automated definition of the domain's
structure is beneficial both in terms of construction time and quality of the
generated graph. The proposed method is extensively validated on three public
datasets (WebNLG, NYT and DocRED) by comparing it with two reference methods
based on CNNs and RNNs models. The evaluation shows the efficiency of our model
in this task. Focusing on scientific document understanding, we present a new
health domain dataset based on publications extracted from PubMed and we
successfully utilize our method on this. Lastly, we demonstrate how this work
lays the foundation for fully automated and unsupervised KG generation.
