  With the advent of semantic web, various tools and techniques have been
introduced for presenting and organizing knowledge. Concept hierarchies are one
such technique which gained significant attention due to its usefulness in
creating domain ontologies that are considered as an integral part of semantic
web. Automated concept hierarchy learning algorithms focus on extracting
relevant concepts from unstructured text corpus and connect them together by
identifying some potential relations exist between them. In this paper, we
propose a novel approach for identifying relevant concepts from plain text and
then learns hierarchy of concepts by exploiting subsumption relation between
them. To start with, we model topics using a probabilistic topic model and then
make use of some lightweight linguistic process to extract semantically rich
concepts. Then we connect concepts by identifying an "is-a" relationship
between pair of concepts. The proposed method is completely unsupervised and
there is no need for a domain specific training corpus for concept extraction
and learning. Experiments on large and real-world text corpora such as BBC News
dataset and Reuters News corpus shows that the proposed method outperforms some
of the existing methods for concept extraction and efficient concept hierarchy
learning is possible if the overall task is guided by a probabilistic topic
modeling algorithm.
