 Enhancing information source selection using a genetic algorithm and social taggingFatma Zohra Lebib, Hakima Mellah and Habiba Drias International Journal of Information Management, 2017, vol. 37, issue 6, 741-749 Abstract: The selection of information sources in a distributed information retrieval environment remains a critical issue. In this context, it is known that a distributed information retrieval system consists of a huge number of sources. Ensuring retrieval effectiveness is to search only sources which are likely to contain relevant information for a query. An important number of heuristics exist among which we quote genetic algorithm that is used to solve the above problem. The proposed genetic algorithm consists in finding the best selection in large space of potential solutions; where a solution is represented as a combination of a set of sources. The improvement of selection accuracy is assured based on the user’s track through the use of sources, to say that source description is enriched with tags from the tagging history. Keywords: Information sources selection; Distributed information retrieval; Bio-inspired methods; Genetic algorithms; Social tagging (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:37:y:2017:i:6:p:741-749 DOI: 10.1016/j.ijinfomgt.2017.07.011 Access Statistics for this article International Journal of Information Management is currently edited by Yogesh K. Dwivedi More articles in International Journal of Information Management from Elsevier |
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