MULTI-AGENT LATENT SEMANTIC INTERNET TECHNOLOGY FOR THE FORMATION OF A SUBJECT-ORIENTED KNOWLEDGE MODEL
DOI:
https://doi.org/10.15588/1607-3274-2021-3-14Keywords:
Internet resources, information search, Zipf’s laws, Grebner bases, intelligent agents, weighted descriptors, latent semantic analysis, multi-agent automatic search procedure.Abstract
Context. The article proposes a latent-semantic technology for extracting information from Internet resources, which allows processing information in natural language, as well as a multi-agent search algorithm based on it. The relevance of this approach to the search for subject-oriented information determined by the fact that currently a direct lexical comparison of queries with document indexes does not fully satisfy the developer. The object of the study is a multi-agent latent-semantic algorithm for searching for subject-oriented information.
Objective. The work is to increase the efficiency of forming a knowledge model that is adequate for this subject area.
Method. A latent semantic technology based on the weighted descriptor method developed by the authors is proposed. The main difference from the existing methods is that the analysis of words occurring in the text both in frequency and taking into account semantics carried out by selecting the appropriate descriptors, which improves the quality of the information found.
Results. The developed latent-semantic technology of information search tested in the task of constructing a knowledge model of automated decision support systems for operational and dispatching control of urban engineering networks. The conducted modeling of the search for subject-oriented information in this subject area showed the effectiveness of the developed approach.
Conclusions. Improving the efficiency of search and semantic content of subject-oriented information of the knowledge model of this subject area achieved by using the weighted descriptor method based on Zipf’s laws in this technology. The prospects for further research are to build evolutionary models of knowledge and improve the quality of updated information.
References
Chu H., Rosenthal M. Search engines for the World Wide Web: A comparative study and evaluation methodology, Proceedings of the annual meeting-american society for information science: journal, 2009, Vol. 33, pp. 127–135.
Singhal A. Modern Information Retrieval: “A brief Overview”, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2001, Vol. 24, No. 4, pp. 35–43.
Gandal N. The dynamics of competition in the internet search engine market, International journal of industrial organization, 2001, Vol. 19, pp. 1103–1117. doi:10.1016/S0167-7187(01)00065-0
Tarakeswar M. K., Kavitha M. D. Search Engines:A Study // Journal of Computer Applications (JCA) : journal, 2011, Vol. 4, No. 1, pp. 29–33.
Mikhalev A. I., Stenin A. A., Shitikova I. G., Lemeshko V. A. Intellectual multi-agent system of formation of the subject-oriented evolutionary model of knowledge, System technologies, 2018, No. 3 (116), pp. 57–63.
Agirre E., Cer D., Diab M. et al. A pilot on semantic textual similarity, The 6-th International Workshop on Semantic Evaluation, Аtlanta, USA, 2012, pp. 385–393.
Bao J., Shen J., Liu X., et al. Semantic Sequence Kin: A Method of Document Copy Detection, Advances In Knowledge Discovery and Data Mining. Lecture Notes in Artificial Intelligence (LNAI). Sydney, Australia, 2004, Vol. 3056, pp. 529–538.
Floridi L. Semantic Web, A Philosophical Assessment, Episteme, 2009, Vol. 6, No. 1, pp. 25–37.
Berners-Lee T., Hendler J., Lassila O. The semantic web, Scientific American, 2001, pp. 29–37.
Kalchenko D. Intelligent agents of semantic Web, Computer press-confer, 2004, No. 10, pp. 26–32.
Etzioni O., Weld D., “Intelligent agents on the internet/ O.Etzioni Weld, Fact, Fiction, and Forecast”, IEEE Expert, No. 4, 1995, pp. 44–49.
Wentia Li. Random Texts Exhibit Zipf’s Law, Like Word Frequency Distribution Santa Fe institute. NM 87501, 1992, Vol. 38, No. 6, pp. 1842–1845.
Kechedzhy K. E., Ustenko O. V., Yampol’ski V. A. Rank distributions of words in additive many-step Markov chains and the Zipf, Physical review, 2005, Vol.72, pp. 1–6.
Gerdt V. P. Groebner bases and innovative methods for algebraic and differential equations, Mathematics and computers in modelling, 1997, Vol. 25, No. 8/9, pp. 75–90
Orlov A.I. Organizational and economic modeling. P.2: Expert estimations. Moscow, Bauman Moscow State Technical University 2011, 486 p.
Golub, J. Matrix calculus. Moscow, Mir, 1999, 548 p.
Alston S. Hausholder Unitary triangularization of an asymmetric matrix, Journal of New Technologies in Computational Systems, 1958, ACM, 5 (4), pp. 339–342. DOI:10.1145/320941.320947
Jones K. S. Statistical interpretation of term specificity and its application to search, Journal. MCB University Documentation, 2004. Vyp. 60, № 5, pp. 493–502.
Matthews D., Curtis D., Fink K. Numerical Methods. Using MATLAB. Numerical Methods: Using MATLAB. 3rd ed. Moscow, Williams Publisher, 2001, 720 p.
Charles Henry Edwards Penney, David E. Differential Equations and the Eigenvalue Problem: Modeling and Computation with Mathematica, Maple and MATLAB.3rd edition. Moscow, Williams Publishing House, 2007, 1104 p.
Alexa M., Zuell C. Text Analysis Software: Commonalities, Differences and Limitations, The Results of a Review, Springer Netherlands, 2000, Vol. 34 (3), pp. 299–321.
Dubinsky A. G. Model of multi-agent information retrieval system in the global network, Artificial intelligence, 1999, No. 3, pp. 271–279.
Stenin A. A., Pasko V. P., Lemeshko V. A. Neurosemantic approach to building automated information retrieval systems, Adaptive automatic control systems, 2019, No. 1(34), pp. 125–130.
Kirichok P. O., Strutinskii S. V., Ol’inik V. G. Special methods of scientific research. National Technical University of Ukraine “Kyiv Polytechnic Institute”. Kyiv, ArtEk, 2016, 592 p. ISBN 978-617-7264-28-5
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 A. A. Stenin, V. P. Pasko, M. A. Soldatova, I. G. Drozdovich
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.