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Application of a Multi-domain Knowledge Structure: The Decisional DNA

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Intelligent Systems for Knowledge Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 252))

Abstract

Knowledge engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. Knowledge engineering techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting. This chapter presents the application of a knowledge structure to different fields of study by constructing Decisional DNA. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its versatility is shown in this chapter when applied to decisional domains in finances and energy. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and share ability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after showing the results, we conclude that the Decisional DNA, as a unique structure, can be applied to multi-domain systems while enhancing predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

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References

  1. Feigenbaum, E., McCorduck, P.: The Fifth Generation. Addison-Wesley, Reading (1983)

    Google Scholar 

  2. Asif, M., Muneer, T.: Energy supply, its demand and security issues for developed and emerging economies. Renewable and Sustainable Energy Reviews 111, 388–413 (2007)

    Google Scholar 

  3. Chau, K.W.: A review on the integration of artificial intelligence into coastal modelling. Journal of Environmental Management 80, 47–57 (2006)

    Article  Google Scholar 

  4. Chau, K.W.: A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin 52, 726–733 (2006)

    Article  Google Scholar 

  5. Kalogirou, S.: Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science 29, 515–566 (2003)

    Article  Google Scholar 

  6. Kalogirou, S.: Artificial Intelligence in energy and renewable energy systems. Nova Publisher, New York (2007)

    Google Scholar 

  7. Kyung, S.P., Soung, H.K.: Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review. Engineering Applications of Artificial Intelligence 12, 121–134 (1998)

    Google Scholar 

  8. Pavlidis, N.G., Tasoulis, D.K., Plagianakos, V.P., Vrahatis, M.N.: Computational intelligence methods for financial time series modeling. International Journal of Bifurcation and Chaos 16(7), 2053–2062 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  9. Liping, L., Shenoy, C., Shenoy, P.P.: Knowledge representation and integration for portfolio evaluation using linear belief functions. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 36(4), 774–785 (2006)

    Article  Google Scholar 

  10. Kirkos, E., Spathis, C., Manolopoulos, Y.: Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications: An International Journal 32(4), 995–1003 (2007)

    Article  Google Scholar 

  11. Lee, C.H.L., Liu, A., Chen, W.S.: Pattern discovery of fuzzy time series for financial prediction. IEEE Transactions on Knowledge and Data Engineering 18(5), 613–625 (2006)

    Article  Google Scholar 

  12. Kim, K.J.: Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications 30(3), 519–526 (2006)

    Article  Google Scholar 

  13. Sanin, C., Szczerbicki, E.: Experience-based Knowledge Representation SOEKS. Cybernetics and Systems 40(2), 99–122 (2009)

    Article  MATH  Google Scholar 

  14. Drucker, P.: The Post-Capitalist Executive: Managing in a Time of Great Change. Penguin, New York (1995)

    Google Scholar 

  15. Noble, D.: Distributed situation assessment. In: Arabnia, P.H.R. (ed.) FUSION 1998, pp. 478–485. University of Georgia (1998)

    Google Scholar 

  16. Deveau, D.: No brain, no gain: Knowledge management. Computing Canada 28, 14–15 (2002)

    Google Scholar 

  17. Ferruci, D., Lally, A.: Building an example application with the unstructured information management architecture. IBM Systems Journal 43(2), 455–475 (2004)

    Article  Google Scholar 

  18. Sanin, C., Szczerbicki, E.: Knowledge supply chain system: A conceptual model. In: Szuwarzynski, A. (ed.) Knowledge management: Selected issues, Gdansk, Poland, pp. 79–97. University Press (2004)

    Google Scholar 

  19. Awad, E., Ghaziri, H.: Knowledge management. Prentice Hall, Englewood Cliffs (2004)

    Google Scholar 

  20. Nonaka, I., Takeuchi, H.: The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press, New York (1995)

    Google Scholar 

  21. Levesque, H.: Knowledge representation and reasoning. Annual Review of Computer Science 1, 255–287 (1986)

    Article  MathSciNet  Google Scholar 

  22. Sowa, J.F.: Preface to knowledge representation, http://www.jfsowa.com/krbook/krpref.htm

  23. Arnold, W., Bowie, J.: Artificial Intelligence: A Personal Commonsense Journey. Prentice Hall, New Jersey (1985)

    Google Scholar 

  24. Sanin, C., Szczerbicki, E.: Extending set of experience knowledge structure into a transportable language extensible markup language. International Journal of Cybernetics and Systems 37(2-3), 97–117 (2006)

    Article  Google Scholar 

  25. Sanin, C., Toro, C., Szczerbicki, E.: An OWL ontology of set of experience knowledge structure. Journal of Universal Computer Science 13(2), 209–223 (2007)

    Google Scholar 

  26. Lloyd, J.W.: Logic for learning: Learning comprehensible theories from structure data. Springer, Berlin (2003)

    Google Scholar 

  27. Malhotra, Y.: From information management to knowledge management: Beyond the ’hi-tech hidebound’ systems. In: Srikantaiah, K., Koening, M.E.D. (eds.) Knowledge management for the information professional, Information Today Inc., New Jersey, pp. 37–61 (2000)

    Google Scholar 

  28. Goldratt, E.M., Cox, J.: The Goal. Grover, Aldershot (1986)

    Google Scholar 

  29. Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal of Human-Computer Studies 43(5-6), 907–928 (1995)

    Article  Google Scholar 

  30. Antoniou, G., Harmelen, F.V.: Web ontology language: OWL. In: Handbook on Ontologies in Information Systems, pp. 67–92. Springer, Heidelberg (2003)

    Google Scholar 

  31. Sevilmis, N., Stork, A., Smithers, T., et al.: Knowledge Sharing by Information Retrieval in the Semantic Web. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 471–485. Springer, Heidelberg (2005)

    Google Scholar 

  32. Toro, C., Sanín, C., Szczerbicki, E., Posada, J.: Reflexive Ontologies: Enhancing Ontologies with self-contained queries. Cybernetics and Systems: An International Journal 39, 1–19 (2008)

    Article  Google Scholar 

  33. Blakeslee, S.: Lost on Earth: Wealth of Data Found in Space. New York Times, C1, March 20 (1990)

    Google Scholar 

  34. Corti, L., Backhouse, G.: Acquiring qualitative data for secondary analysis. Forum: Qualitative Social Research 6, 2 (2005)

    Google Scholar 

  35. Humphrey, C.: Preserving research data: A time for action. In: Preservation of electronic records: new knowledge and decision-making: postprints of a conference - symposium 2003, pp. 83–89. Canadian Conservation Institute, Ottawa (2004)

    Google Scholar 

  36. Johnson, P.: Who you gonna call? Technicalities 10(4), 6–8 (1990)

    Google Scholar 

  37. Sanin, C., Szczerbicki, E.: Extending Set of Experience Knowledge Structure into a Transportable Language XML (eXtensible Markup Language). Cybernetics and Systems 37(2), 97–117 (2006)

    Article  Google Scholar 

  38. Zhang, Z.: Ontology query languages for the semantic Web. Master’s thesis. University of Georgia, Athens (2005)

    Google Scholar 

  39. Energy Information Administration. Official Energy Statistics from the US Government, http://www.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.html

  40. UCI Machine Learning Repository. Adult Data Set, http://archive.ics.uci.edu/ml/datasets/Adult

  41. Reiner, B., Hahn, K.: Optimized Management of Large-Scale Data Sets Stored on Tertiary Storage Systems. IEEE Distributed Systems Online 5(5), 1–8 (2004)

    Article  Google Scholar 

  42. Chen, Y.J., Chen, Y.M., Chu, H.C., Kao, H.Y.: On technology for functional requirement-based reference design retrieval in engineering knowledge management. Decision Support Systems 44, 798–816 (2008)

    Google Scholar 

  43. Kohlhase, M., Sucan, I.: A Search Engine for Mathematical Formulae. In: Calmet, J., Ida, T., Wang, D. (eds.) AISC 2006. LNCS (LNAI), vol. 4120, pp. 241–253. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  44. Cobos, Y., Toro, C., Sarasua, C., Vaquero, J., Linaza, M.T., Posada, J.: An Architecture for Fast Semantic Retrieval in the Film Heritage Domain. In: 6th International Workshop on Content-Based Multimedia Indexing (CBMI), London, UK, pp. 272–279 (2008)

    Google Scholar 

  45. Amar, K.D., Wei, W., McGuinness, D.L.: Industrial Strength Ontology Management. In: Cruz, I., et al. (eds.) The Emerging Semantic Web, vol. 75, pp. 101–118. IOS Press, Amsterdam (2002)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering and Built Environment School of Mechanical Engineering, The University of Newcastle, University Drive, Callaghan, NSW, 2308

    Cesar Sanín, Leonardo Mancilla-Amaya, Edward Szczerbicki & Paul CayfordHowell

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  1. Cesar Sanín
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  2. Leonardo Mancilla-Amaya
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  3. Edward Szczerbicki
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  4. Paul CayfordHowell
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Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str.Wyb.Wypsianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Faculty of Management and Economics, Gdansk University of Technology, Str.Narutowicza 11/12, 80-233, Gdansk, Poland

    Edward Szczerbicki

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Sanín, C., Mancilla-Amaya, L., Szczerbicki, E., CayfordHowell, P. (2009). Application of a Multi-domain Knowledge Structure: The Decisional DNA. In: Nguyen, N.T., Szczerbicki, E. (eds) Intelligent Systems for Knowledge Management. Studies in Computational Intelligence, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04170-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-04170-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04169-3

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