Rank fusion and semantic genetic notion based automatic query expansion model

Query expansion term selection methods are really very important for improving the accuracy and efficiency of pseudo-relevance feedback based automatic query expansion for information retrieval system by removing irrelevant and redundant terms from the top retrieved feedback documents corpus with respect to user query. Individual query expansion term selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual query expansion term selection method that would outperform other individual query expansion term selection methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual query expansion term selection methods. Second, we propose a model for combining multiple query expansion term selection methods by using rank combination approach, called multiple ranks combination based query expansion. Third, semantic filtering is used to filter semantically irrelevant term obtained after combining multiple query expansion term selection methods, called ranks combination and semantic filtering based query expansion. Fourth, the genetic algorithm is used to make an optimal combination of query terms and candidate term obtained after rank combination and semantic filtering approach, called semantic genetic filtering and rank combination based query expansion. Our experimental results demonstrated that our proposed approaches achieved significant improvement over each individual query expansion term selection method and related state-of-the-art approaches.