  Most of the existing information retrieval systems are based on bag of words
model and are not equipped with common world knowledge. Work has been done
towards improving the efficiency of such systems by using intelligent
algorithms to generate search queries, however, not much research has been done
in the direction of incorporating human-and-society level knowledge in the
queries. This paper is one of the first attempts where such information is
incorporated into the search queries using Wikipedia semantics. The paper
presents an essential shift from conventional token based queries to concept
based queries, leading to an enhanced efficiency of information retrieval
systems. To efficiently handle the automated query learning problem, we propose
Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based
queries are learnt using a co-evolving evolutionary procedure. Learning concept
based queries using an intelligent evolutionary procedure yields significant
improvement in performance which is shown through an extensive study using
Reuters newswire documents. Comparison of the proposed framework is performed
with other information retrieval systems. Concept based approach has also been
implemented on other information retrieval systems to justify the effectiveness
of a transition from token based queries to concept based queries.
