Abstract
Query expansion (QE) has long been suggested as an effective way to improve the retrieval effectiveness and overcome the shortcomings of search engines. Notwithstanding its performance, QE still suffers from limitations that have limited its deployment as a standard component in search systems. Its major drawback is the retrieval efficiency, especially for large-scale data sources. To overcome this issue, we first put forward a new modeling of query expansion with a new and original metaheuristic namely, Bat-Inspired Approach to improve the computational cost. Then, this approach is used to retrieve both the best expansion keywords and the best relevant documents simultaneously unlike the previous works where these two tasks are performed sequentially.
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Khennak, I., Drias, H. (2016). Bat Algorithm for Efficient Query Expansion: Application to MEDLINE. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-31232-3_11
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DOI: https://doi.org/10.1007/978-3-319-31232-3_11
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