An Enhanced Method for Computation of Similarity between the Contexts in Trust Evaluation Using Weighted Ontology

In the context-aware trust evaluation, most of the times ontology trees are employed to represent the relation among contexts. Then, the similarity between two contexts is computed according to the contexts' distance in their ontology tree. Therefore, the performance of these methods is dependent on the tree's structure and how balanced the ontology tree is constructed, which is a limitation for them. In an unbalanced ontology tree, one branch of a node is split generally while the other branch is split in more details. As a result, this unbalanced ontology tree negatively affects all computations of the mentioned methods. To overcome this limitation, we presented a weighted ontology tree, which is balanced and independent of the tree's structure. In the proposed tree, each edge is labeled with the similarity distance between its corresponding nodes. To achieve this, we used an approach, which is based on the WorldNet English lexical reference system. Finally, the similarity between two arbitrary contexts in their weighted ontology tree is computed according to their weighted similarity distance in the tree. Having the contexts similarities, trust value for a new context is computed based on the previously experienced contexts.