  Ontologies have been known for their semantic representation of knowledge.
ontologies cannot automatically evolve to reflect updates that occur in
respective domains. To address this limitation, researchers have called for
automatic ontology generation from unstructured text corpus. Unfortunately,
systems that aim to generate ontologies from unstructured text corpus are
domain-specific and require manual intervention. In addition, they suffer from
uncertainty in creating concept linkages and difficulty in finding axioms for
the same concept. Knowledge Graphs (KGs) has emerged as a powerful model for
the dynamic representation of knowledge. However, KGs have many quality
limitations and need extensive refinement. This research aims to develop a
novel domain-independent automatic ontology generation framework that converts
unstructured text corpus into domain consistent ontological form. The framework
generates KGs from unstructured text corpus as well as refine and correct them
to be consistent with domain ontologies. The power of the proposed
automatically generated ontology is that it integrates the dynamic features of
KGs and the quality features of ontologies.

Artificial Intelligence