Searching educational and research content has been made easier by popular online repositories such as NCBI, google scholar, etc. For a user query, they generally return thousands of results and also rank them. However, these tools often produce results that are distantly related to user query goals and often overwhelm the users with the volume of results. In recent years, researchers are experimenting with content-specific databases and thereby producing few but more relevant query results. This project creates an exact snapshot of the PubMed database from NCBI and provides users with novel search options that sort and display the results on the basis of matching percentages between query keywords and article titles. The proposed system uses Natural Language Processing (NLP) techniques to aid users to form search queries in plain English that would otherwise only be possible through setting up advanced search options. The experiment results were promising and found to produce more relevant query results compared to the state-of-the-art methods.