A novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performance
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Date
2010
Journal Title
Journal ISSN
Volume Title
Publisher
Medwell Journals
Abstract
The significant roles play by ranking function in the performance and success of Information Retrieval (IR) systems and search engines cannot be underestimated. Diverse ranking functions are available in IR literature. However, empirical studies show that ranking functions do not perform constantly well across different contexts (queries, collections, users). In this study, a novel three-stage integrated ranking framework is proposed for implementing discovering, optimizing and inference rankings used in IR systems. The first phase, discovery process is based on Genetic Programming (GP) approach which smartly combines structural and contents features in the documents while the second phase, optimization process is based on Genetic Algorithm (GA) which combines document retrieval scores of various well-known ranking functions. In the 3rd phase, Fuzzy inference proves as soft search constraints to be applied on documents. We demonstrate how these two features are combined to bring new tasks and processes within the three concept stages of integrated framework for effective IR.
Description
Keywords
Ranking function, Information retrieval, Evolutionary techniques, Fuzzy inference system, Data fusion method
Citation
Agbele, K.K., et al. (2010). A novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performance. International Journal of Soft Computing, 5(3): 155-163