Coordinate Indexing Algorithms Taking Into Account the Classification Signs-terms in the Subject Area
( Pp. 21-31)

More about authors
Zhaxybayev Darkhan O. doctoral student at the Department of Information Systems
L.N. Gumilyov Eurasian National University
Nur-Sultan, Republic of Kazakhstan Barakhnin Vladimir B. Dr. Sci. (Eng.); Leading Researcher at the Laboratory of Information Resources, Head at the Department of Mathematical Modeling, Faculty of Mechanics and Mathematics
Federal Research Center for Information and Computational Technologies; Novosibirsk State University
Novosibirsk, Russian Federation
For read the full article, please, register or log in
Libraries and Internet searchers are becoming increasingly frustrated with topical access results, in part because of the unmanageability of a wide range of search engines. The need to improve accuracy and limit the size of search queries was the main motivation for this study. The purpose of this article is to explore coordinate indexing algorithms taking into account the classification features-terms in the subject area. The study was written using the material modelling method (mathematical): pre-existing models were used, which offer partial coordination as fundamental to confirm their effectiveness in practice. The measurement method was used to analyse the numerical results. In the presented study, the features and characteristics of partial coordination were presented. In addition, the benefits of partial coordination have been studied. The methodology proposed by the authors focuses on the benefits of a “smart” query that can be executed in an OPAC environment without further effort on the part of the user; the user enters their keywords as before, but “smart” keyword coordination in documents avoids unsuitable partial matches. It was concluded that the order of ranking of documents on the subject should be included in the same way with those unverified records lacking partial coordination of partial documents.
How to Cite:
Zhaxybayev D.O., Barakhnin V.B., (2022), COORDINATE INDEXING ALGORITHMS TAKING INTO ACCOUNT THE CLASSIFICATION SIGNS-TERMS IN THE SUBJECT AREA. Computational Nanotechnology, 1: 21-31. DOI: 10.33693/2313-223X-2022-9-1-21-31
Reference list:
Armitage J.E., Lynch M.F. Some structural characteristics of articulated subject indexes // Information Storage and Retrieval. 1968. Vol. 4. Pp. 101-111.
Barash Y., Guralnik G., Tau N. et al. Comparison of deep learning models for natural language processing-based classification of non-English head CT reports // Neuroradiology. 2020. Vol. 62. No. 10. Pp. 1247-1256.
Beaulieu M., Borgman C.L. A new era for OPAC research: Introduction to the special topics issue on current research in online public access systems // Journal of the American Society for Information Science. 1996. Vol. 47. No. 7. Pp. 491-492.
Chakraborty A., Pawar A., Jang H. et al. A real-time feature indexing system on live video streams // 44th IEEE Annual Computers, Software, and Applications Conference. Madrid: Institute of Electrical and Electronics Engineers Inc. 2020. Pp. 42-50.
Croft W.B. Approaches to intelligent information retrieval // Information Processing Management. 1987. Vol. 23. No.4. Pp. 249-254.
Endres T., Kranzdorf L., Schneider V., Renkl A. It matters how to recall - task differences in retrieval practice // Instructional Science. 2020. Vol. 48. No. 6. Pp. 699-728.
Gavit B.K. Web based library services // Library Philosophy and Practice. 2019. Vol. 2019. P. 2931.
Guo J., Fan Y., Pang L. et al. A deep look into neural ranking models for information retrieval // Information Processing and Management. 2020. Vol. 57. No. 6. P. 102067.
Gupta D., Berberich K. Optimizing hyper-phrase queries // 10th International Conference on the Theory of Information Retrieval. New York: Association for Computing Machinery. 2020. Pp. 41-48.
Hosey C., Vujovic L., St. Thomas B. et al. Just give me what I want: How people use and evaluate music search // Conference on Human Factors in Computing Systems. Glasgow: Association for Computing Machinery. 2019. P. 147770.
Ioannakis G., Koutsoudis A., Pratikakis I., Chamzas C. RETRIEVAL - an online performance evaluation tool for information retrieval methods // IEEE Transactions on Multimedia. 2018. Vol. 20. No. 1. Pp. 119-127.
Jiang J., Han R., Meng X., Li K. TSASC: Tree-seed algorithm with sine-cosine enhancement for continuous optimization problems // Soft Computing. 2020. Vol. 24. No. 24. Pp. 18627-18646.
Kalinauskaite D. Detecting information-dense texts: Towards an automated analysis // International Conference on Information Technologies. 2018. Vol. 2145. Pp. 95-98.
Kanev A.I., Terekhov V.I. Evaluation issues of query result ranking for semantic search // 7th International Young Scientists Conference on Information Technology. 2020. Vol. 1694. No. 1. P. 012004.
Kiss A.N., Libaers D., Barr P.S. et al. CEO cognitive flexibility, information search, and organizational ambidexterity // Strategic Management Journal. 2020. Vol. 41. No. 12. Pp. 2200-2233.
Kumar R., Singh J., Singh B., Rana M.K. Usability of OPAC in university libraries // Library Philosophy and Practice. 2018. Vol. 1. Pp. 1-11.
Li W., Zhang S., Qi G. A graph-based approach for resolving incoherent ontology mappings // Web Intelligence. 2018. Vol. 16. No. 1. Pp. 15-35.
Liu C., Huai H. An improved full-text retrieval for elementary education resource database system // Journal of Physics: Conference Series. 2020. Vol. 1693. No. 1. P. 012053.
Luong D.D., Phuong V.Q., Tung H.D.T. A new indexing technique XR tree for bio informatics XML data compression // International Journal of Engineering and Advanced Technology. 2019. Vol. 8. No. 5. Pp. 1168-1173.
Mehta K., Foster I., Klasky S. et al. A codesign framework for online data analysis and reduction // The International Conference for High Performance Computing, Networking, Storage and Analysis. Denver: Institute of Electrical and Electronics Engineers Inc, 2019. Pp. 11-20.
Moffat A., Scholer F., Yang Z. Estimating measurement uncertainty for information retrieval effectiveness metrics // Journal of Data and Information Quality. 2018. Vol. 10. No. 3. P. 10.
Murzin F., Perfliev A., Shmanina T. Methods of syntactic analysis and comparison of constructions of a natural language oriented to use in search systems // Bulletin of the Novosibirsk Computing Center, Series: Computer Science. 2010. No. 31. Pp. 91-109.
Nori R., Palmiero M., Giusberti F. et al. Web searching and navigation: Age, intelligence, and familiarity // Journal of the Association for Information Science and Technology. 2020. Vol. 71. No. 8. Pp. 902-915.
Steinberg D., Metz P. User response to and knowledge about an online catalog // College Research Libraries. 1984. Vol. 45. No. 1. Pp. 66-70.
Thomas P., Billerbeck B., Craswell N., White R.W. Investigating searchers mental models to inform search explanations // ACM Transactions on Information Systems. 2019. Vol. 38. No. 1. P. 10.
Yi L., Yuan R., Long S., Xue L. Expert information automatic extraction for IoT knowledge base // Procedia Computer Science. 2019. Vol. 147. Pp. 288-294.
Bakhturina T.A., Sukiasyan E.R. Modern cataloging terminology. Moscow: Moskva, 1992.
Lezin G.V., Tuzov V.A. Semantic analysis of the text in Russian: semantic-syntactic model of the sentence. Economic and Mathematical Research: Mathematical Models and Information Technologies. St. Petersburg: Nauka. 2003. Pp. 282-303.
Nekrestyanov I.S. Thematic-oriented methods of information retrieval. St. Petersburg: Saint Petersburg State University. 2000.
Nozhov I.M. Morphological and syntactic processing of text (models and programs). Moscow: Moskva, 2003.
Shirokov A.V. Development of a model of informational portrait of a user for personalized search. Reports of the Competition of Scientific Projects in the Field of Information Search “Internet Mathematics”. 2017 [Electronic resource]. URL: report2007.xml (data of accesses: 26.12.2020).
post-coordination, partial coordination, subject directory, search engine, Boolean query.