  The continuous increasing in the amount of the published and stored
information requires a special Information Retrieval (IR) frameworks to search
and get information accurately and speedily. Currently, keywords-based
techniques are commonly used in information retrieval. However, a major
drawback of the keywords approach is its inability of handling the polysemy and
synonymy phenomenon of the natural language. For instance, the meanings of
words and understanding of concepts differ in different communities. Same word
use for different concepts (polysemy) or use different words for the same
concept (synonymy). Most of information retrieval frameworks have a weakness to
deal with the semantics of the words in term of (indexing, Boolean model,
Latent Semantic Analysis (LSA) , Latent semantic Index (LSI) and semantic
ranking, etc.). Traditional Arabic Information Retrieval (AIR) models
performance insufficient with semantic queries, which deal with not only the
keywords but also with the context of these keywords. Therefore, there is a
need for a semantic information retrieval model with a semantic index structure
and ranking algorithm based on semantic index.
