|Article title||KNOWLEDGE SIFTER MODEL FOR SEMANTIC IDENTIFICATION PROBLEMS|
|Authors||Yu. A. Kravchenko|
|Section||SECTION IV. DATA ANALYSIS AND KNOWLEDGE MANAGEMENT|
|Month, Year||04, 2018 @en|
|Abstract||This article is devoted to solving the problem of knowledge intellectual accumulation and processing of abstract universal models constructing by the search and decision-making semantics formalization in the knowledge processing. A special role in the solution of this problem plays the reduction of information uncertainty in the information flow accompanying the process of knowledge accumulation and the use of appropriate means for information analyzing to perform the necessary optimization procedures, forecasting and study the feasibility of the results. Concrete scientific result is agent model of knowledge filter which can solve problems of semantic identification of key information and processing of heterogeneous knowledge resources on the basis of ontology-based structures. The application of semantic identification procedure as a tool for the classification analysis of knowledge requires, first of all, the clarification of generic concepts that will serve as identifiers in the selection of the appropriate lexical-semantic groups of terms. Elements of knowledge in the network resources are depersonalized, which leads to desemantization, i.e. attributing the meaning to the term, which the term did not have before, and then using the term in a new meaning, at the same time flouting the old meaning. This raises the problem of semantic identification, which requires a complex and lengthy learning of the linguistic features of the source being processed to reflect the meaning and construct a system of knowledge elements relations on the set. The solution of semantic ambiguity problem required the development of ontological methods for finding a one-to-one correspondence of the identical and similar.|
|Keywords||Semantic models; knowledge search and processing; agent models; knowledge sifter; semantic identification; ontological structure.|
|References||1. Lutsan M.V., Nuzhnov E.V., Kureichik V.V. Self-learning of the containers service coordinator agent in multi-agent automation environment of transit cargo terminal, Advances in Intelligent Systems and Computing, 2015, Vol. 347, pp. 109-117.
2. Amerland D. Google Semantic Search: Search Engine Optimization (SEO) Techniques That Gets Your Company More Traffic, Increases Brand Impact and Amplifies Your Online Presence. Que Publishing, 2013, 230 p.
3. Bova V.V., Kravchenko Y.A., Kureichik V.V. Decision Support Systems for Knowledge Management // Software Engineering in Intelligent Systems. Proceedings of the 4th Computer Science On-line Conference 2015 (CSOC2015). Vol. 3. Springer International Publishing AG Switzerland, 2015, pp. 123-130.
4. Bova V.V., Kravchenko Y.A., Kureichik V.V. Development of Distributed Information Systems: Ontological Approach, Software Engineering in Intelligent Systems. Proceedings of the 4th Computer Science On-line Conference 2015 (CSOC2015). Vol. 3. Springer International Publishing AG Switzerland, 2015, pp. 113-122.
5. Kravchenko Y.A., Kureichik V.V. Knowledge management based on multi-agent simulation in informational systems, Conference proceedings. 8th IEEE International Conference “Application of Information and Communication Technologies – AICT 2014”. 15-17 October 2014, Astana, Kazakhstan, pp. 264-267.
6. Gladkov L.A., Gladkova N.V., Legebokov A.A. Organization of knowledge management based on hybrid intelligent methods, Advances in Intelligent Systems and Computing, 2015, Vol. 349, pp. 107-112.
7. Kerschberg L., Jeong H., Kim W. Emergent Semantic in Knowledge Sifter: An Evolutionary Search Agent based on Semantic Web Services. In: Spaccapietra, S., Aberer, K., Cudre-Mauroux, P. (eds.), Journal on Data Semantic VI. LNCS. 2006, Vol. 4090, pp. 187-209.
8. Kureychik V.M., Kureychik V.V. Evolyucionnye, sinergeticheskie i gomeostaticheskie strategii v iskusstvennom intellekte: sostoyanie i perspektivy [Evolutionary, synergetic and homeostatic strategies in artificial intelligence: state and prospects], Novosti iskusstvennogo intellekta [News of artificial intelligence], 2000, No. 3, pp. 39-67.
9. Eberhart RC, Shi Y. Particle swarm optimization: developments, applications and resources, In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Seoul, Korea, 2001.
10. Bova V.V., Kureychik V.V. Integrirovannaya podsistema gibridnogo i kombinirovannogo poiska v zadachakh proektirovaniya i upravleniya [Integrated subsystem of the hybrid and combined search in problems of design and management], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 12 (113), pp. 37-43.
11. Zaporozhec D.Yu., Kureychik V.V. Gibridnyy algoritm resheniya zadach transportnogo tipa
[A hybrid algorithm for solving problems of transport type], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2013, No. 7 (144), pp. 80-85.
12. Hu X., Shi Y., Eberhart R.C. Recent Advences in Particle Swarm, In Proceedings of Congress on evolutionary Computation (CEC), Portland, Oregon, 2004, pp. 90-97.
13. Gladkov L.A., Kureychik V.M., Kureychik V.V. Geneticheskie algoritmy [Genetic algorithm]. Moscow: Fizmatlit, 2006, 320 p.
14. Zaporozhets D.U., Zaruba D.V. and Kureichik V.V. Representation of solutions in genetic VLSI placement algorithms, IEEE East-West Design & Test Symposium – (EWDTS’2014) Kiev, Ukraine, 2014, pp. 1-4.
15. Sousa T., Silva A., Neves A. Particle Swarm based Data Mining Algorithms for classification tasks, Parallel Computing, 2004, Vol. 30, Issues 5-6, pp. 767-783.
16. Kureychik V.V., Rodzin S.I. O pravilakh predstavleniya resheniy v evolyucionnykh algo-ritmakh [About the rules for the submission of solutions in evolutionary algorithms], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 7 (108), pp. 13-21.
17. Zaporozhec D.Yu., Kudaev A.Yu., Lezhebokov A.A. Mnogourovnevyy algoritm resheniya zadachi parametricheskoy optimizacii na osnove bioinspirirovannykh evristik [A multilevel algorithm for solving the problem of parametric optimization based on bio-inspired heuristics], Izvestiya Kabardino-Balkarskogo nauchnogo centra RAN [Izvestiya of Kabardino-Balkar scientific center of RAS], 2013, No. 4 (54), pp. 21-28.
18. Kureychik V.M. Osobennosti postroeniya sistem podderzhki prinyatiya resheniy [Features of construction of systems of support of acceptance of decisions], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 7 (132), pp. 92-98.
19. Kureychik V.M., Kazharov A.A. Ispol'zovanie shablonnykh resheniy v murav'inykh algoritmakh [The use of standard solutions in ant colony optimization algorithms] Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2013, No. 7 (144), pp. 11-17.
20. Gladkov L.A., Kravchenko Y.A., Kureichik V.V. Evolutionary Algorithm for Extremal Subsets Comprehension in Graphs, World Applied Sciences Journal, 2013, Vol. 27 (9), pp. 1212-1217.