abstract = "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 Wikipedia-based Evolutionary Semantics
(Wiki-ES) framework for generating concept based
queries using a set of relevance statements provided by
the user. The query learning is handled by a
co-evolving genetic programming procedure. To evaluate
the proposed framework, the system is compared to a
bag-of-words based genetic programming framework as
well as to a number of alternative document filtering
techniques. The results obtained using Reuters newswire
documents are encouraging. In particular, the injection
of Wikipedia semantics into a GP-algorithm leads to
improvement in average recall and precision, when
compared to a similar system without human knowledge. A
further comparison against other document filtering
frameworks suggests that the proposed GP-method also
performs well when compared with systems that do not
rely on query-expression learning.",