A New Modeling of Query Expansion Using an Effective Bat-Inspired Optimization Algorithm

One of the most successful techniques to improve the retrieval effectiveness and overcome the shortcomings of search engines is Query Expansion (QE). Despite its effectiveness, QE still suffers from drawbacks that have limited its deployment as a standard component in search systems. Its major weakness is the computational cost, especially for large-scale data sources. To cope with this issue, we first propose in this paper, a judicious modeling of query expansion with a new and original metaheuristic namely, Bat-Inspired Approach to enhance the retrieval efficiency. Next, this approach is used to find both the best expansion keywords and the best relevant documents simultaneously unlike the previous works where these two tasks are performed sequentially. Our computational experiments undertaken on MEDLINE, the on-line medical database, show that our approach significantly enhances the retrieval efficiency over state-of-the-art methods.