This paper examines the capability of using information Bases (KBs) in automatic reasoning for superior facts analysis. KBs, as a form of information representation, are uniquely suited for automated reasoning as they shop various kinds of domain-specific statistics in a single pool of declarative facts. The paper explores how to reliably and efficiently use such declarative data shops connecting one of a kind forms of artificial evidence and techniques. Right here, a new technique is proposed that unites the efficiency of RDF and the expressiveness of text-primarily based documents for representing the research context. An extension of the RDF format to represent reasoning steps and knowledge entities extra as it should be is proposed and tested on a repository of actual-global clinical and genetics datasets. The outcomes prove that the approach can improve computation of diverse components of studies workflow consisting of gene-disorder mapping tasks, whilst retaining affordable computational complexity. The proposed KB extends the capacity to generate efficient and powerful records analysis for research contexts and may result in new paradigms of automated reasoning