Domain-specific knowledge-based information retrieval model using knowledge reduction

Information is a meaningful collection of data. Information retrieval (IR) is an important tool for changing data into 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 the SVM IR classical model does not convey sufficient relevancy between a query and documents to produce effective results reflecting knowledge except when using term frequency (tf) and inverse document frequency ( idf).; Knowledge is organized information imbued by intelligence. To augment the IR process with knowledge, several techniques have been 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.; We created an information retrieval model that incorporates domain-specific knowledge to provide knowledgeable answers to users. We used 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.