  Information content (IC) based measures for finding semantic similarity is
gaining preferences day by day. Semantics of concepts can be highly
characterized by information theory. The conventional way for calculating IC is
based on the probability of appearance of concepts in corpora. Due to data
sparseness and corpora dependency issues of those conventional approaches, a
new corpora independent intrinsic IC calculation measure has evolved. In this
paper, we mainly focus on such intrinsic IC model and several topological
aspects of the underlying ontology. Accuracy of intrinsic IC calculation and
semantic similarity measure rely on these aspects deeply. Based on these
analysis we propose an information theoretic framework which comprises an
intrinsic IC calculator and a semantic similarity model. Our approach is
compared with state of the art semantic similarity measures based on corpora
dependent IC calculation as well as intrinsic IC based methods using several
benchmark data set. We also compare our model with the related Edge based,
Feature based and Distributional approaches. Experimental results show that our
intrinsic IC model gives high correlation value when applied to different
semantic similarity models. Our proposed semantic similarity model also
achieves significant results when embedded with some state of the art IC models
including ours.
