Once the list of documents is formed, a semantic similarity value between the query and each document is computed by the system. The ranking algorithm of the system combines two factors which are the concept weight specified in the user's query and its relevance to a document. All concept names (or instance names) returned by the inference engine form a query vector. In VSM, a document vector Xj in the extended Term Document Matrix (TDM) calculated is ranked according to the similarity between it and the query vector q. Then a vector of index term weights is calculated. These weights,พ are calculated by most often used tf-idf scheme as shown in the equation (1) below where t represent time, f represent frequency and id represent id of a document: