Information is a meaningful collection of data. Information retrieval (IR) is an important tool for changing data to information. Of the three classical IR models (Boolean, Support Vector Machine, and Probabilistic), the Support Vector Machine (SVM) IR model is most widely used. But this model does not convey enough relevancies between a query and documents to produce effective results reflecting knowledge. To augment the IR process with knowledge, several techniques are proposed including query expansion by using a thesaurus, a term relationship measurement like Latent Semantic Indexing (LSI), and a probabilistic inference engine using Bayesian Networks. Our research aims to create an information retrieval model that incorporates domain specific knowledge to provide knowledgeable answers to users. We use a knowledge-based model to represent domain specific knowledge. Unlike other knowledge-based IR models, our model converts domain-specific knowledge to a relationship of terms represented as quantitative values, which gives improved efficiency.