  In today's era of information explosion, more users are becoming more reliant
upon recommender systems to have better advice, suggestions, or inspire them.
The measure of the semantic relatedness or likeness between terms, words, or
text data plays an important role in different applications dealing with
textual data, as in a recommender system. Over the past few years, many
ontologies have been developed and used as a form of structured representation
of knowledge bases for information systems. The measure of semantic similarity
from ontology has developed by several methods. In this paper, we propose and
carry on an approach for the improvement of semantic similarity calculations
within a recommender system based-on RDF graphs.
