Aim of this project is to augment a range of algorithms and models to the existing search framework to explore a middle ground between keyword based search and semantic exploration. The intent is to aid the search process by building concept maps. Concept maps are graphical tools for organization nd representation of information. A Concept map is composed of elements called concepts. Concepts are labeled by a couple of words or phrases. The relationship between the elements in a map represents the underlying corpus in a meaningful way. This is can be considered analogous to summary statistics while dealing with numbers. Individual numbers may not make a lot of sense but statistical information about the group may throw greater light. The goal is to increase the search effectiveness, by presenting the users with concepts; in addition to documents matching the given query. These concepts will be derived from the search result. Equipped with these concepts which present a high level view of the underlying documents, the users should be able to search large body of text with ease. In learning process, when a learner, supported by an appropriate environment, shall be able to attach a new concept to those existent inside his/her cognitive structure. This phenomenon is called subsumption. The project is aimed to apply the same for searching and text exploration. The core idea is to make the search a more natural phenomenon rather than a forced one by enhancing the search experience of the information seeker.