MULTI-AGENT LATENT SEMANTIC INTERNET TECHNOLOGY FOR THE FORMATION OF A SUBJECT-ORIENTED KNOWLEDGE MODEL

Authors

  • A. A. Stenin Kiev Polytechnic Institute. Igor Sikorsky, Kiev, Ukraine., Ukraine
  • V. P. Pasko Kyiv Polytechnic Institute. Igor Sikorsky, Kiev, Ukraine., Ukraine
  • M. A. Soldatova Kiev Polytechnic Institute. Igor Sikorsky, Kiev, Ukraine., Ukraine
  • I. G. Drozdovich Institute of telecommunications and global information space of the National Academy of Sciences of Ukraine., Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2021-3-14

Keywords:

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.

Author Biographies

A. A. Stenin, Kiev Polytechnic Institute. Igor Sikorsky, Kiev, Ukraine.

Dr. Sc., Professor of the Department of technical Cybernetics.

V. P. Pasko, Kyiv Polytechnic Institute. Igor Sikorsky, Kiev, Ukraine.

PhD, Associate Professor, Department of technical Cybernetics.

M. A. Soldatova, Kiev Polytechnic Institute. Igor Sikorsky, Kiev, Ukraine.

PhD, Senior lecturer of the Department of technical Cybernetics.

I. G. Drozdovich, Institute of telecommunications and global information space of the National Academy of Sciences of Ukraine.

PhD, Senior Scientific Associate.

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

2021-10-09

How to Cite

Stenin, A. A., Pasko, V. P., Soldatova, M. A., & Drozdovich, I. G. (2021). MULTI-AGENT LATENT SEMANTIC INTERNET TECHNOLOGY FOR THE FORMATION OF A SUBJECT-ORIENTED KNOWLEDGE MODEL . Radio Electronics, Computer Science, Control, (3), 166–174. https://doi.org/10.15588/1607-3274-2021-3-14

Issue

Section

Progressive information technologies