  Ontology Learning (OL) is the computational task of generating a knowledge
base in the form of an ontology given an unstructured corpus whose content is
in natural language (NL). Several works can be found in this area most of which
are limited to statistical and lexico-syntactic pattern matching based
techniques Light-Weight OL. These techniques do not lead to very accurate
learning mostly because of several linguistic nuances in NL. Formal OL is an
alternative (less explored) methodology were deep linguistics analysis is made
using theory and tools found in computational linguistics to generate formal
axioms and definitions instead simply inducing a taxonomy. In this paper we
propose "Description Logic (DL)" based formal OL framework for learning factual
IS-A type sentences in English. We claim that semantic construction of IS-A
sentences is non trivial. Hence, we also claim that such sentences requires
special studies in the context of OL before any truly formal OL can be
proposed. We introduce a learner tool, called DLOL_IS-A, that generated such
ontologies in the owl format. We have adopted "Gold Standard" based OL
evaluation on IS-A rich WCL v.1.1 dataset and our own Community representative
IS-A dataset. We observed significant improvement of DLOL_IS-A when compared to
the light-weight OL tool Text2Onto and formal OL tool FRED.
