Use of N-grams Model and Semantic Similarity to Improve the Results of Search Engine

Semantic indexing and document similarity is a significant problem of the information retrieval systems (IRS). Recently many works use semantics in information retrieval systems to find the information easily. A search engine is the best example of the application of information retrieval to get the most relevant results. In this work, to facilitate the use of search engines for users for getting the most relevant documents, we propose a new approach for calculating the semantic similarity between user's query and a list of documents based on the N-grams model (i.e., a sub-sequence of n constructed elements from a given sequence). Results show that our method give us better results than some methods from the literature.