Article

Article title LAYERED ARCHITECTURE KNOWLEDGE MANAGEMENT SCRIPT BASED ON ONTOLOGICAL ANALYSIS
Authors Yu.A. Kravchenko
Section SECTION III. MODELING AND DESIGN
Month, Year 02, 2015 @en
Index UDC 002.53:004.89
DOI
Abstract Article is devoted to the development of layered architecture knowledge management script as the principal organization of information processes and their relationships, as well as the principles of their design and evolution. Information management is viewed as a set of processes systematic acquisition, synthesis and sharing of knowledge. An approach based on knowledge representation of ontological analysis under uncertainty. Under the accumulation of knowledge is understood as the process of transferring knowledge from disparate sources into a data warehouse by using different methods, models, algorithms, and tools. This process requires a maximum automation, as under different strategies of knowledge acquisition is always a problem of information transmission in the joint work of the expert in the subject area and knowledge engineering. Despite the pronounced specificity of subject areas, ontology should be built as a chain of interrelated processes that will provide integrated nature of intellectual knowledge management system. At the stage of identifying the areas of expertise necessary to first define a set of study characteristics. Next, you need to choose a priori information sources and begin to form a knowledge base and data warehouse, which later will set the relationship between the categories of knowledge. As a source of knowledge is most easily connected database operational information systems through a mechanism for creating data warehouses. Similarly connected system of electronic and non-electronic document archives, which can be centralized and decentralized control scheme. Integrating knowledge from different sources may be based on the ontology requirements for the development of which will be a pre-formed sheets.

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Keywords Ontological analysis; intelligent systems; knowledge management systems; information processes; decision support.
References 1. Khoroshevskiy V.F. Semanticheskie tekhnologii: ozhidaniya i trendy [Semantic technologies: expectations and trends], Open semantic technologies for intelligent systems (OSTIS-2012) [Open semantic technologies for intelligent systems (OSTIS-2012)], pp. 143-158.
2. Tuzovskiy A.F., Chirikov S.V., Yampol'skiy V.Z. Sistemy upravleniya znaniyami (metody i tekhnologii) [The knowledge management system (methods and technologies)], Under the editorship V.Z. Yampol'skogo. Tomsk: Izd-vo NTL, 2005, 260 p.
3. Chernyakhovskaya L.R., Malakhova A.I. Razrabotka modeley i metodov intellektual'noy podderzhki prinyatiya resheniy na osnove ontologii organizatsionnogo upravleniya programmnymi proektami [Development of models and methods for intelligent decision support ontology-based organizational management of software projects], Ontologiya proektirovaniya [Ontology of Designing], 2013, No. 4 (10), pp. 42-52.
4. Gladun A.Ya., Rogushina Yu.V. Ontologii v korporativnykh sistemakh [Ontology in enterprise systems], Korporativnye sistemy [Corporate system], 2006, No. 1.
5. Guarino N., Welty C.A. Towards a Metotodology for Ontology - Model Engineering, Proceeding of the ECOOP-2000 Workshop on Model Engineering (eds. by Bezivin J. and Ernst J.), 2000. Available at: http://www.metamodel.com/IWME00/articles/guarino.pdf.
6. Gangeni A., Pisanelli D.M., Steve G. An Overview of the ONIONS Project: Applying Ontologies to the Integration of Medical Terminologies, Data & Knowledge Engineering, 1999, Vol. 31, pp. 183-220.
7. Palagin A.V., Petrenko N.G. Sistemno-ontologicheskiy analiz predmetnoy oblasti [System-ontological domain analysis], USiM [Control Systems and Machines], 2009, No. 4, pp. 3-14.
8. Ivlev Yu.V. Logika: uchebnik dlya vuzov [Logic: textbook for universities]. Moscow: Prospekt, 2010, 296 p.
9. Tel'nov Yu.F. Intellektual'nye informatsionnye sistemy [Intelligent information systems]. Moscow: Moskovskiy gosudarstvennyy universitet ekonomiki, statistiki i informatiki, 2008, 26 p.
10. Kravchenko Yu.A., Zaporozhets D.Yu., Lezhebokov A.A. Sposoby intellektual'nogo analiza dannykh v slozhnykh sistemakh [Methods data mining in complex systems], Izvestiya KBNTs RAN [Izvestiya Kabardino-Balkaria Scientific Centre of the RAS], 2012, No. 3 (47), pp. 52-57.
11. Kureichik V.M., Rodzin S.I. Evolutionary algorithms: genetic programming, Journal of Computer and Systems Sciences International, 2002, Vol. 41, No. 1, pp. 123-132.
12. 12. 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.
13. Kravchenko Yu.A., Bova V.V. Nechetkoe modelirovanie raznorodnykh znaniy v intellektual'nykh obuchayushchikh sistemakh [Fuzzy modeling of heterogeneous knowledge in intelligent tutoring systems], Otkrytoe obrazovanie [Open Education], 2013, No. 4 (99), pp. 70-74.
14. Kureychik V.M. Osobennosti postroeniya sistem podderzhki prinyatiya resheniy [Features of decision making support system design], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 7 (132), pp. 92-98.
15. Kureychik V.V., Rodzin S.I. O pravilakh predstavleniya resheniy v evolyutsionnykh algoritmakh [On the rules for the submission decisions in evolutionary algorithm], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 7 (108), pp. 13-21.
16. Qing He, Xiu-Rong Zhao, Ping Luo, Zhong-Zhi Shi. Combination methodologies of multi-agent hyper surface classifiers: design and implementation issues, Second international work-shop, AIS-ADM 2007, Proceedings. Springer Berlin Heidelberg, 2007, pp. 100-113.
17. A.De Nicola, Missikoff M., r.Navigli. A software engineering approach to ontology building, Information systems, 2009, Vol. 34, pp. 258-275,
18. Guarino N., Oberle D., Staab S. What is an Ontology, Handbook on Ontologies. Springer, 2009, pp. 1-17.
19. Yang X.-S. A new metaheuristic sat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization (NISCO’2010), Berlin: Springer, 2010, Vol. 284, pp. 65-74.
20. Sarraipa J., et al. Semantic Enrichment of Standard-based Electronic Catalogues, 13th IFAC Symposium on Information Control Problems in Manufacturing, 2009.
21. Kerschberg L., Kim W., Scime A. Personalizable semantic taxonomy-based search agent. USA: George Mason Intellectual Properties, INC (Fairfax, VA), 2006.
22. Kerschberg L., Jeong H., Kim W. Emergent Semantic in Knowledge Sifter: An Evolutionary Search Agent based on Semantic Web Services, Journal on Data Semantics. VI. LNCS, 2006, Vol. 4090, pp. 187-209.

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