Trust Evaluation using an Improved Context Similarity Measurement

Mohsen Raeesi, Mohammad Amin Morid, Mehdi Shajari

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.

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