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Development of Knowledge-Based Systems Which Use Bayesian Networks

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Synergies Between Knowledge Engineering and Software Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 626))

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Abstract

Bayesian networks allow for a concise graphical representation of decision makers’ knowledge on an uncertain domain. However, there are no well-defined methodologies showing how to use a Bayesian network as the core of a knowledge-based system, even less if not all the features should be supported by the knowledge model. That is to say, the software, that has to be released to customers, has also to embed functionalities not based on knowledge, concerning to the information management processes closer to the world of a classical software development projects. These components of the software application have to be built according to practices and methods of Software Engineering discipline. This chapter is conceived as a guideline about how to manage and intertwine languages and techniques related to Knowledge Engineering and Software Engineering in order to build a knowledge based system supported by Bayesian networks.

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References

  1. Abdullah, M., Benest, I., Paige, R., Kimble, C.: Using Unified Modeling Language for Conceptual Modelling of Knowledge-Based Systems. In: Parent, C., Schewe, K.-D., Storey, V., Thalheim, B. (eds.) Conceptual Modeling - ER 2007. Lecture Notes in Computer Science, vol. 4801, pp. 438–453. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  2. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 28–37 (2001)

    Article  Google Scholar 

  3. Buchanan, B.G., Barstow, D., Bechtal, R., Bennett, J., Clancey, W., Kulikowski, C., Mitchell, T., Waterman, D.A.: Constructing an expert system. Build. Expert Syst. 50, 127–167 (1983)

    Google Scholar 

  4. Buntine, W.L.: A guide to the literature on learning probabilistic networks from data. Knowledge and data engineering. IEEE Trans. Knowl. Data Eng. 8(2), 195–210 (1996)

    Article  Google Scholar 

  5. Cañadas, J., del Águila, I.M., Palma, J.: Development of a web tool for action threshold evaluation in table grape pest management. Precis. Agric. 1–23 (to appear) (2016)

    Google Scholar 

  6. Cañadas, J., Palma, J., Túnez, S.: A tool for MDD of rule-based web applications based on OWL and SWRL. In: Nalepa, G.J., Baumeister, J. (eds.) Proceedings of the 6th Workshop on Knowledge Engineering and Software Engineering, vol. 636. http://CEUR-WS.org (2010)

  7. Cañadas, J., Palma, J., Túnez, S.: Defining the semantics of rule-based Web applications through model-driven development. Appl. Math. Comput. Sci. 21(1), 41–55 (2011)

    Google Scholar 

  8. Cestnik, B.: Estimating probabilities: a crucial task in machine learning. In: Proceedings of the European Conference on Artificial Inteligence (ECAI’90), pp. 147–149 (1990)

    Google Scholar 

  9. Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347 (1992)

    MATH  Google Scholar 

  10. del Águila, I.M., Cañadas, J., Palma, J., Túnez, S.: Towards a methodology for hybrid systems software development. In: Proceedings of the Eighteenth International Conference on Software Engineering & Knowledge Engineering (SEKE’2006), pp. 188–193. San Francisco (2006)

    Google Scholar 

  11. del Águila, I.M., del Sagrado, J., Túnez, S., Orellana, F.J.: Seamless software development for systems based on bayesian networks - an agricultural pest control system example. In: Moinhos-Cordeiro, J.A., Virvou, M., Shishkov, B. (eds.) ICSOFT 2010 Proceedings of the Fifth International Conference on Software and Data Technologies, Vol. 2, pp. 456–461. SciTePress (2010)

    Google Scholar 

  12. del Águila, I.M., del Sagrado, J.: Metamodeling of Bayesian networks for decision-support systems development. In: Nalepa, G.J., Cañadas, J. Baumeister, J. (eds.) KESE 2012 Proceedings of 8th Workshop on Knowledge Engineering and Software Engineering at the 20th Biennial European Conference on Artificial Intelligence (ECAI 2012), CEUR Workshop proceedings, vol. 949, pp. 12–19 (2010)

    Google Scholar 

  13. del Águila, I.M., Palma, J., Túnez, S.: Milestones in software engineering and knowledge engineering history: a comparative review. Sci. World J. (2014)

    Google Scholar 

  14. del Águila, I.M., Cañadas, J., Túnez, S.: Decision making models embedded into a web-based tool for assessing pest infestation risk. Biosyst. Eng. 133, 102–115 (2015)

    Article  Google Scholar 

  15. del Sagrado, J., del Águila, I.M., Orellana, F.J.: Architecture for the use of synergies between knowledge engineering and requirements engineering. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds.) Advances in Artificial Intelligence, vol. 7023, pp. 213–222. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  16. del Sagrado, J., Túnez, S., del Águila, I.M., Orellana, F.J.: Architectural model for agrarian software management with decision support features. Adv. Sci. Lett. 19(10), 2958–2961 (2013)

    Article  Google Scholar 

  17. Drapeau, S., Madiot, F., Brazeau, J.F., Dugré, P.L.: SmartEA: Una herramienta de arquitectura empresarial basada en las técnicas MDE. Novática 228, 21–28 (2014)

    Google Scholar 

  18. Druzdzel, M.J., Roger, R.F.: Decision support systems. In: Broy, A.K. (ed.) Encyclopedia of Library and Information Science, pp. 120–133. Marcel Dekker, New York (2000)

    Google Scholar 

  19. Elvira Consortium: Elvira: an environment for creating and using probabilistic graphical models. In: Gámez, J.A., Salmerón, A. (eds.) Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM-02), pp. 223–230 (2002)

    Google Scholar 

  20. Ernst, G.W., Newell, A.: GPS: A Case Study in Generality and Problem Solving. Academic Press, Cambridge (1969)

    MATH  Google Scholar 

  21. Fikes, R.E., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3–4), 189–208 (1971)

    Article  MATH  Google Scholar 

  22. Gašević, D., Djurić, D., Devedžić, V.: Model Driven Architecture and Ontology Development. Springer, New York (2006)

    Google Scholar 

  23. Giachetti, G., Valverde, F., Pastor, O.: Improving automatic UML2 profile generation for MDA industrial development. In: Song, I.Y., et al. (eds.) Advances in Conceptual Modeling - Challenges and Opportunities, ER 2008 Workshops. Lecture Notes in Computer Science, vol. 5232, pp. 113–122. Springer, Heidelberg (2008)

    Google Scholar 

  24. Gómez-Pérez, A., Fernández-López, M., Corcho, O.: Ontological Engineering: With Examples From the Areas of Knowledge Management. E-Commerce and the Semantic Web. Springer, Heidelberg (2006)

    Google Scholar 

  25. Harman, M., Mansouri, S.A., Zhang, Y.: Search-based software engineering: trends, techniques and applications. ACM Comput. Surv. 45(1), A1–64 (2012)

    Article  Google Scholar 

  26. Hart, P.E., Duda, R.O., Einaudi, M.T.: PROSPECTOR a computer-based consultation system for mineral exploration. Math. Geol. 10(5), 589–610 (1978)

    Article  Google Scholar 

  27. Hussmann, H., Meixner, G., Zuehlke, D.: Model-Driven Development of Advanced User Interfaces. Springer, New York (2011)

    Book  Google Scholar 

  28. Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, New York (2007)

    Book  MATH  Google Scholar 

  29. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (2005)

    MATH  Google Scholar 

  30. Kjaerulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, New York (2008)

    Book  MATH  Google Scholar 

  31. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2009)

    Google Scholar 

  32. Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence. CRC Press, Boca Raton (2010)

    Google Scholar 

  33. Maher, M.L., Allen, R.H.: Expert System Components. In: Maher, M.L. (ed.) Expert Systems for Civil Engineers: Technology and Application, pp. 3–13. American Society of Civil Engineering (1987)

    Google Scholar 

  34. Moreno, N., Romero, J.R., Vallecillo, A.: An overview of model-driven web engineering and the MDA. In: Rossi, G., Pastor, O., Schwabe, D., Olsina, L. (eds.) Web Engineering: Modelling and Implementing Web Applications, pp. 353–382. Springer, London (2008)

    Chapter  Google Scholar 

  35. Neapolitan, R.E.: Learning Bayesian Networks. Prentice-Hall, New Jersey (2004)

    Google Scholar 

  36. Newell, A.: The knowledge level. Artif. Intell. 18(1), 87–127 (1982)

    Article  MathSciNet  Google Scholar 

  37. Object Management Group.: MDA Guide Version 1.0.1. OMG document: omg/2003-06-01 (2003)

    Google Scholar 

  38. Orellana, F.J., del Sagrado, J., del Águila, I.M.: SAIFA: a web-based system for integrated production of olive cultivation. Comput. Electron. Agric. 78(2), 231–237 (2011)

    Article  Google Scholar 

  39. Papajorgji, P.J., Pardalos, P.M.: Software Engineering Techniques Applied to Agricultural Systems: An Object-Oriented and UML Approach. Springer, New York (2014)

    Book  Google Scholar 

  40. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)

    MATH  Google Scholar 

  41. Schmidt, D.C.: Guest editor’s introduction: model-driven engineering. Computer 39(2), 25–31 (2006)

    Article  Google Scholar 

  42. Shortliffe, E.H.: MYCIN: Computer-Based Medical Consultations. Elsevier, New York (1976)

    Google Scholar 

  43. Spirtes, P., Glymour, C., Scheines, R.: An algorithm for fast recovery of sparse causal graphs. Soc. Sci. Comput. Rev. 9, 62–72 (1991)

    Article  Google Scholar 

  44. Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1), 161–197 (1998)

    Article  MATH  Google Scholar 

  45. Studer, R., Fensel, D., Decker, S., Benjamins, V.R.: Knowledge engineering: survey and future directions. In: German Conference on Knowledge-Based Systems, pp. 1–23. Springer (1999)

    Google Scholar 

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Acknowledgements

This research has been financed by the Spanish Ministry of Economy and Competitiveness under project TIN2013-46638-C3-1-P and partially supported by Data, Knowledge and Software Engineering (DKSE) research group (TIC-181) of the University of Almería.

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Authors and Affiliations

  1. University of Almería, Crta de la Playa, 04120, Almería, Spain

    Isabel M. del Águila & José del Sagrado

Authors
  1. Isabel M. del Águila
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  2. José del Sagrado
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Correspondence to Isabel M. del Águila .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology , Kraków, Poland

    Grzegorz J. Nalepa

  2. Universität Würzburg , Würzburg, Germany

    Joachim Baumeister

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del Águila, I.M., del Sagrado, J. (2018). Development of Knowledge-Based Systems Which Use Bayesian Networks. In: Nalepa, G., Baumeister, J. (eds) Synergies Between Knowledge Engineering and Software Engineering. Advances in Intelligent Systems and Computing, vol 626. Springer, Cham. https://doi.org/10.1007/978-3-319-64161-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-64161-4_3

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  • Online ISBN: 978-3-319-64161-4

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