Abstract
This paper is devoted to the problem of automated evaluation of relevance of search queries results. High relevance of search algorithm output is the base of effective large quantities of data processing, which is worked at by users of modern informational systems. Automated and reliable estimate of relevance of search queries results will give the opportunity to lower time expenditures for the best algorithm choice. The usage of improved from this perspective algorithms will allow to raise effectiveness and user satisfaction when dealing with automatic search systems in any activities.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Gabrilovich, E., Markovitch, S.: Feature generation for text categorization using world knowledge. IJCAI 5, 1048–1053 (2005)
Samsonovich, A.V.: Functional possible biologically inspired by cognitive architectures. In: XVII All-Russian Scientific-Technical Conference “Neuroinformatics-2015”. National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow (2015)
Samsonovich, A.V., Klimov, V.V., Rybina, G.V.: Biologically inspired cognitive architectures (BICA) for young scientists. In: Proceedings of the First International Early Research Career Enhancement School (FIERCES 2016) (2016). ISBN: 978-3-319-32553-8 (Print) 978-3-319-32554-5 (Online)
Hjorland, B.: The foundation of the concept of relevance. J. Am. Soc. Inf. Sci. Technol. (2010)
Skorokhodov, I.S., Tikhomirova, A.N.: Key stages of text processing and feature generation in text classification. Probl. Mod. Sci. Educ. 15(57), 18–22 (2016)
Balakrishnan, V., Lloyd-Yemoh, E.: Stemming and lemmatization: acomparison of retrieval performances. IACSIT (2014)
Balandina, A., Chernyshov, A., Klimov, V., Kostkina, A.: Usage of language particularities for semantic map construction: affixes in Russian language. In: International Symposium on Neural Networks, ISNN 2016, Advances in Neural Networks – ISNN, pp. 731–738 (2016)
Klimov, V.V., Chernyshov, A.A., Shchukin, B.A.: Composition of web-services based on semantic description. In: WEBIST 2015 – Proceedings 11th International Conference on Web Information Systems and Technologies (2015)
Altman, J.M., Bland, D.G. Statistics notes: measurement error. BMJ (1996)
Tikhomirova, A.N., Sidorenko, E.V.: Optimization of the process of scientific and technical expertise projects in nanobiomedical technologies. Nanotechnics 1(29), 26–28 (2012)
Kryanev, A.V., Tikhomirova, A.N., Sidorenko, E.V.: Group expertise of innovative projects using the Bayesian approach. Econ. Mathe. Methods 49(2), 134–139 (2013)
Acknowledgments
This work was supported by Competitiveness Growth Program of the Federal Autonomous Educational Institution of Higher Professional Education National Research Nuclear University MEPhI (Moscow Engineering Physics Institute).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Tikhomirova, A., Matrosova, E. (2018). Algorithms for Intelligent Automated Evaluation of Relevance of Search Queries Results. In: Samsonovich, A., Klimov, V. (eds) Biologically Inspired Cognitive Architectures (BICA) for Young Scientists. BICA 2017. Advances in Intelligent Systems and Computing, vol 636. Springer, Cham. https://doi.org/10.1007/978-3-319-63940-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-63940-6_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63939-0
Online ISBN: 978-3-319-63940-6
eBook Packages: EngineeringEngineering (R0)