Context-based weighting for vector space model to evaluate the relation between concept and context in information storage and retrieval system

In this paper, we represent the context based weighting scheme for vector space model to evaluate the relation between concept and context in Information storage and retrieval system. A meaning of a word is relatively decided by a context dynamically. A vector space model, generally use a static weighting scheme for term document matrix like latent semantic indexing (LSI), co-occurrence or correlations of terms etc. It is important to weight each element of each vector by a context. It is necessary to understand a concept by not reading one data but summarizing large data. Therefore, the features in the vector space model should be generate from data set corresponding to represent a certain thing. That is, we should generate vectors for the vector space model dynamically corresponding to a context and data distribution. The features of our method are a run time calculation of each element of vectors in a vector space model corresponding to a context. Our method measure the similarity between target concept vector and context vector.