  In context-aware trust evaluation, using ontology tree is a popular approach
to represent the relation between contexts. Usually, similarity between two
contexts is computed using these trees. Therefore, the performance of trust
evaluation highly depends on the quality of ontology trees. Fairness or
granularity consistency is one of the major limitations affecting the quality
of ontology tree. This limitation refers to inequality of semantic similarity
in the most ontology trees. In other words, semantic similarity of every two
adjacent nodes is unequal in these trees. It deteriorates the performance of
contexts similarity computation. We overcome this limitation by weighting tree
edges based on their semantic similarity. Weight of each edge is computed using
Normalized Similarity Score (NSS) method. This method is based on frequencies
of concepts (words) co-occurrences in the pages indexed by search engines. Our
experiments represent the better performance of the proposed approach in
comparison with established trust evaluation approaches. The suggested approach
can enhance efficiency of any solution which models semantic relations by
ontology tree.
