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Toward integration of knowledge based systems and knowledge discovery systems
Keywords:
Data Mining, Expert Systems, Knowledge discovery, Knowledge based systems, Systems architecturesAbstract
This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advance of the technologies involved.
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References
[1] Sierra, E., Hossian, A. y García-Martínez, R. 2003. Sistemas Expertos que Recomiendan Estrategias de Instrucción. Un Modelo para su Desarrollo. Revista Latinoamericana de Tecnología Educativa. 1(1): 19-30.
[2] Sierra, E., Hossian, A., García-Martínez, R. y Marino, P.2005. Sistema Experto para Control Inteligente de las Variables Ambientales de un
Edificio Energéticamente Eficiente. Proc. XI Reunión de Trabajo en Procesamiento de la Información y Control. pp. 446-452.
[3] Bermejo, F., Britos, P., Rossi, B y García Martínez, R. 2002. Sistema de Asistencia para la Configuración de Ventiladores OAF en Neonatolgía. Revista del Instituto Tecnológico de Buenos Aires. 28: 24-68.
[4] Gómez, S., Perichinsky, G. y Garcia Martinez, R. 2001. Un Sistema Experto Legal para la Individualización y Acuerdos para Penas. Proc. Simposio Argentino de Informática y Derecho. pp. 23-33.
[5] Diez, E., Britos, P., Rossi, By García-Martínez, R. 2003. Generación Asistida del Mapa de Actividades de Proyectos de Desarrollo de Software. Reportes Técnicos en Ingeniería del Software. 5(1):13-18.
[6] García-Martínez, R. y Britos, P. 2004. Ingeniería de Sistemas Expertos. Editorial Nueva Librería.
[7] Britos, P. 2001. Sistema de Ayuda sobre Legislación Argentina en Riesgos de Trabajo”. Tesis de Magíster en Ingeniería del Conocimiento. Facultad de Informática de la Universidad Politécnica de Madrid.
[8] Rizzi, M. 2001. Sistema Experto Asistente de Requerimientos. Tesis de Magister en Ingeniería del Software. Escuela de Posgrado del Instituto Tecnológico de Buenos Aires.
[9] Ierache, J. y Garcia-Martinez, R. 2004. Sistema Experto Aplicado al Control del Espacio Aéreo. Proc. IX Congreso Argentino de Ciencias de la Computación.
[10] Hossian, A. 2003. Sistema de Asistencia para la Selección de Estrategias y Actividades Instruccionales. Tesis de Magister en Ingeniería del Software. Escuela de Posgrado del Instituto Tecnológico de Buenos Aires.
[11] Debenham, J. 1990. Knowledge Systems Design. Prentice Hall.
[12] Debenham, J. 1998. Knowledge Engineering: Unifying Knowledge Base and Database Design. Springer-Verlag.
[13] Gomez, A., Juristo, N., Montes, C. y Pazos, J. 1997. Ingeniería del Conocimiento. Editorial R. Areces. Madrid.
[14] Michalski, R. S. 1991. Toward an Unified Theory of Learning: An Outline of Basic Ideas, Proc. 3rd World Conference on the Fundamentals of AI.
[15] DeJong, G.F., Mooney, R.J. 1986. Explanation-Based Learning: An Alternative View, Machine Learning, 1: 145-176.
[16] Michalski, R. S., Carbonell, J. G., Mitchell, T. M. (eds.). 1983. Machine Learning: An Artificial Intelligence Approach, Vol. I. Morgan-Kauffman
[17] Michalski, R. S., Carbonell, J. G., Mitchell, T. M. (eds.), 1986. Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan-Kauffman
[18] Michalski, R. Bratko, I. Kubat, M (eds.) 1998. Machine Learning and Data Mining, Methods and Applications, John Wiley & Sons Ltd, West Sussex, England
[19] Michalski, R. S., Tecuci, G. (eds) 1994. Machine Learning: A Multistrategy Approach, Vol. III, Morgan Kauffman
[20] Mitchell, T. M. 1996. Machine Learning, McGraw-Hill.
[21] Michie, D. 1988. Machine Learning in the next five years, EWSL-88, 3rd European Working Session on Learning, Glasgow, Londres, Pitman.
[22] Fayad, U. M., Piatetsky-Shapiro, G., Smyth, P., Uhturudsamy, R. (eds). 1996. Advances in Knowledge Discovery and Data Mining, San Mateo, AAAI Press.
[23] Grossman, R., Kasif, S., Moore, R., Rocke, D. andUllman, J. 1999. Data Mining Research: Opportunities and Challenges, A Report of three NSF Workshops on Mining Large, Massive, and Distributed Data, January 1999, Chicago
[24] Evangelos, S., Han, J, (eds). 1996. Proc. of the Second International Conference on Knowledge Discovery and Data Mining, Portland, EE.UU.
[25] Jensen D. 2002. Knowledge Evaluation. Handbook of Data Mining and Knowledge Discovery. Kloesgen, W. and J. Zytkow (Eds.). Oxford: Oxford University Press
[26] Utgoff P., V. Lesser, and D. Jensen 2000. Inferring task structure from data. University of Massachusetts, Department of Computer Science. Technical Report UM-CS-2000-054.
[27] Jensen D. and J. Neville 2002. Schemas and models. Proc. Multi-Relational Data Mining Workshop, 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[28] Neville J. and D. Jensen 2002. Supporting relational knowledge discovery: Lessons in architecture and algorithm design. Proc. Data Mining Lessons Learned Workshop, 19th International Conference on Machine Learning.
[29] Jensen D., Y. Dong, B. Lerner, E. McCall, L. Osterweil, S. Sutton Jr., and A. Wise 1999. Coordinating agent activities in knowledge discovery processes. In Proc. International Joint Conference on Work Activities Coordination and Collaboration. pp. 137-146.
[30] Britos, P., Hossian, A., García Martínez, R. y Sierra, E. 2005. Minería de Datos Basada en Sistemas Inteligentes. 876 páginas. Editorial Nueva Librería. ISBN 987-1104-30-8.
[31] Fritz, W., García Martínez, R., Rama, A., Blanqué, J., Adobatti, R, y Sarno, M. 1989. The Autonomous Intelligent System. Robotics and Autonomous Systems, 5(2): 109-125.
[32] García Martínez, R. & Borrajo Millán, D.1996. Unsupervised Machine Learning Embedded in Autonomous Intelligent Systems.Proc. XIV International Conference on Applied Informatics. pp 71-73.
[33] García Martínez, R. y Borrajo Millán, D. 1997. Planning, Learning and Executing in Autonomous Systems. Lecture Notes in Artificial Intelligence. 1348:208-210.
[34] García Martínez, R. & Borrajo Millán, D.1998. Learning in Unknown Environments by Knowledge Sharing. Proc. Seventh European Workshop on Learning Robots. pp 22-32.
[35] García Martínez, R. y Borrajo Millán, D. 2000. An Integrated Approach of Learning, Planning and Executing. Journal of Intelligent and Robotic Systems 29(1):47-78.
[36] Sierra, E., García-Martínez, R., Hossian, A., Britos, P. y Balbuena, E. 2006. Providing Intelligent User-Adapted Control Strategies in Building Environments. Research in Computing Science Journal, 19: 235-241.
[37] Grosser, H., Britos, P. y García-Martínez, R. 2005. Detecting Fraud in Mobile Telephony Using Neural Networks. Lecture Notes in Artificial Intelligence 3533: 613-615.
[38] Felgaer, P., Britos, P. and García-Martínez, R. 2006. Prediction in Health Domain Using Bayesian Network Optimization Based on Induction Learning Techniques. International Journal of Modern Physics C 17(3): 447-455.
[39] Cogliati, M., Britos, P. y García-Martínez, R. (2006). Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT. Springer IFIP Series..
[40] Rancán, C. 2004. Arquitectura de Sistema Híbrido de Evaluación del Alistamiento de Unidades Navales Auxiliares. Reportes Técnicos en Ingeniería del Software. 6(1): 45-54.
[41] La Battaglia, J., Rodriguez, I., Thomas, P., Pesado, P., Bertone, R. 2003. Tecnología aplicada a gestión distribuida. Proc. de las XI Jornadas de Jóvenes Investigadores AUGM 2003. Universidad Nacional de La Plata – Argentina.
[42] Miatón, I., Pesado, P., Bertone, R. y De Giusti. 2003. Agentes Basados en Sistemas Distribuidos. Proc. V Workshop de Investigadores en Ciencia de la Computación.
[43] De Giusti E., Mollo Brisco G., La Battaglia, J.,Pasini, A. y Pesado, P. 2004. Sistema de Simulación de Escenarios y Decisiones Empresarias. Proc. XII Jornadas de Jóvenes Investigadores AUGM 2004. Universidad Federal Do Paraná - Curitiba, Brasil.
[44] Pesado, P. Feierherd G. y Pasini, A. 2005. Requirement Specifications for Electronic Voting System. Journal of Computer Science & Technology, 5(4): 312-319 .
[45] Hoffmann, F., Baesens, B., Mues, C. and Vanthienen, J. 2006. Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research. (in press).
[46] Cao, H., Recknagel, F. Joo, G., Kim, D. 2006. Discoveryof predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA. Ecological Informatics, 1(1): 43-53.
[47] Podgorelec, V., Kokol, P., Stiglic, M., Heričko, M., Rozman, I. 2005. Knowledge discovery with classification rules in a cardiovascular dataset. Computer Methods and Programs in Biomedicine, 80: S39-S49.
[48] Huang, M., Tsou, Y., Lee, S. 2006. Integrating fuzzy data mining and fuzzy artificial neural networks fordiscovering implicit knowledge. Knowledge-Based Systems, 19(6): 396-403.
[49] Kasabov, K. 2006. Adaptation and interaction in dynamical systems: Modelling and rule discovery through evolving connectionist systems. Applied Soft Computing, 6(3): 307-322.
[50] Carpenter, G., Martens, S., Ogas, O. 2005. Self-organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks.Neural Networks, 18(3): 287-295.
[51] Liu, D., Ke, C. 2006. Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning. Expert Systems with Applications. (in press).
[52] Moreno, M., Quintales, L., García, F., Polo, J. 2004. Building knowledge discovery-driven models for decision support in project management. Decision Support Systems, 38(2): 305-317.
[53] Kim, M., Han, I. 2003. The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithmsExpert Systems with Applications, 25(4): 637-646.
[54] Leigh, W., Modani, N., Purvis, R., Roberts, R. 2002. Stock market trading rule discovery using technicalcharting heuristics. Expert Systems with Applications, 23(2): 155-159.
[2] Sierra, E., Hossian, A., García-Martínez, R. y Marino, P.2005. Sistema Experto para Control Inteligente de las Variables Ambientales de un
Edificio Energéticamente Eficiente. Proc. XI Reunión de Trabajo en Procesamiento de la Información y Control. pp. 446-452.
[3] Bermejo, F., Britos, P., Rossi, B y García Martínez, R. 2002. Sistema de Asistencia para la Configuración de Ventiladores OAF en Neonatolgía. Revista del Instituto Tecnológico de Buenos Aires. 28: 24-68.
[4] Gómez, S., Perichinsky, G. y Garcia Martinez, R. 2001. Un Sistema Experto Legal para la Individualización y Acuerdos para Penas. Proc. Simposio Argentino de Informática y Derecho. pp. 23-33.
[5] Diez, E., Britos, P., Rossi, By García-Martínez, R. 2003. Generación Asistida del Mapa de Actividades de Proyectos de Desarrollo de Software. Reportes Técnicos en Ingeniería del Software. 5(1):13-18.
[6] García-Martínez, R. y Britos, P. 2004. Ingeniería de Sistemas Expertos. Editorial Nueva Librería.
[7] Britos, P. 2001. Sistema de Ayuda sobre Legislación Argentina en Riesgos de Trabajo”. Tesis de Magíster en Ingeniería del Conocimiento. Facultad de Informática de la Universidad Politécnica de Madrid.
[8] Rizzi, M. 2001. Sistema Experto Asistente de Requerimientos. Tesis de Magister en Ingeniería del Software. Escuela de Posgrado del Instituto Tecnológico de Buenos Aires.
[9] Ierache, J. y Garcia-Martinez, R. 2004. Sistema Experto Aplicado al Control del Espacio Aéreo. Proc. IX Congreso Argentino de Ciencias de la Computación.
[10] Hossian, A. 2003. Sistema de Asistencia para la Selección de Estrategias y Actividades Instruccionales. Tesis de Magister en Ingeniería del Software. Escuela de Posgrado del Instituto Tecnológico de Buenos Aires.
[11] Debenham, J. 1990. Knowledge Systems Design. Prentice Hall.
[12] Debenham, J. 1998. Knowledge Engineering: Unifying Knowledge Base and Database Design. Springer-Verlag.
[13] Gomez, A., Juristo, N., Montes, C. y Pazos, J. 1997. Ingeniería del Conocimiento. Editorial R. Areces. Madrid.
[14] Michalski, R. S. 1991. Toward an Unified Theory of Learning: An Outline of Basic Ideas, Proc. 3rd World Conference on the Fundamentals of AI.
[15] DeJong, G.F., Mooney, R.J. 1986. Explanation-Based Learning: An Alternative View, Machine Learning, 1: 145-176.
[16] Michalski, R. S., Carbonell, J. G., Mitchell, T. M. (eds.). 1983. Machine Learning: An Artificial Intelligence Approach, Vol. I. Morgan-Kauffman
[17] Michalski, R. S., Carbonell, J. G., Mitchell, T. M. (eds.), 1986. Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan-Kauffman
[18] Michalski, R. Bratko, I. Kubat, M (eds.) 1998. Machine Learning and Data Mining, Methods and Applications, John Wiley & Sons Ltd, West Sussex, England
[19] Michalski, R. S., Tecuci, G. (eds) 1994. Machine Learning: A Multistrategy Approach, Vol. III, Morgan Kauffman
[20] Mitchell, T. M. 1996. Machine Learning, McGraw-Hill.
[21] Michie, D. 1988. Machine Learning in the next five years, EWSL-88, 3rd European Working Session on Learning, Glasgow, Londres, Pitman.
[22] Fayad, U. M., Piatetsky-Shapiro, G., Smyth, P., Uhturudsamy, R. (eds). 1996. Advances in Knowledge Discovery and Data Mining, San Mateo, AAAI Press.
[23] Grossman, R., Kasif, S., Moore, R., Rocke, D. andUllman, J. 1999. Data Mining Research: Opportunities and Challenges, A Report of three NSF Workshops on Mining Large, Massive, and Distributed Data, January 1999, Chicago
[24] Evangelos, S., Han, J, (eds). 1996. Proc. of the Second International Conference on Knowledge Discovery and Data Mining, Portland, EE.UU.
[25] Jensen D. 2002. Knowledge Evaluation. Handbook of Data Mining and Knowledge Discovery. Kloesgen, W. and J. Zytkow (Eds.). Oxford: Oxford University Press
[26] Utgoff P., V. Lesser, and D. Jensen 2000. Inferring task structure from data. University of Massachusetts, Department of Computer Science. Technical Report UM-CS-2000-054.
[27] Jensen D. and J. Neville 2002. Schemas and models. Proc. Multi-Relational Data Mining Workshop, 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[28] Neville J. and D. Jensen 2002. Supporting relational knowledge discovery: Lessons in architecture and algorithm design. Proc. Data Mining Lessons Learned Workshop, 19th International Conference on Machine Learning.
[29] Jensen D., Y. Dong, B. Lerner, E. McCall, L. Osterweil, S. Sutton Jr., and A. Wise 1999. Coordinating agent activities in knowledge discovery processes. In Proc. International Joint Conference on Work Activities Coordination and Collaboration. pp. 137-146.
[30] Britos, P., Hossian, A., García Martínez, R. y Sierra, E. 2005. Minería de Datos Basada en Sistemas Inteligentes. 876 páginas. Editorial Nueva Librería. ISBN 987-1104-30-8.
[31] Fritz, W., García Martínez, R., Rama, A., Blanqué, J., Adobatti, R, y Sarno, M. 1989. The Autonomous Intelligent System. Robotics and Autonomous Systems, 5(2): 109-125.
[32] García Martínez, R. & Borrajo Millán, D.1996. Unsupervised Machine Learning Embedded in Autonomous Intelligent Systems.Proc. XIV International Conference on Applied Informatics. pp 71-73.
[33] García Martínez, R. y Borrajo Millán, D. 1997. Planning, Learning and Executing in Autonomous Systems. Lecture Notes in Artificial Intelligence. 1348:208-210.
[34] García Martínez, R. & Borrajo Millán, D.1998. Learning in Unknown Environments by Knowledge Sharing. Proc. Seventh European Workshop on Learning Robots. pp 22-32.
[35] García Martínez, R. y Borrajo Millán, D. 2000. An Integrated Approach of Learning, Planning and Executing. Journal of Intelligent and Robotic Systems 29(1):47-78.
[36] Sierra, E., García-Martínez, R., Hossian, A., Britos, P. y Balbuena, E. 2006. Providing Intelligent User-Adapted Control Strategies in Building Environments. Research in Computing Science Journal, 19: 235-241.
[37] Grosser, H., Britos, P. y García-Martínez, R. 2005. Detecting Fraud in Mobile Telephony Using Neural Networks. Lecture Notes in Artificial Intelligence 3533: 613-615.
[38] Felgaer, P., Britos, P. and García-Martínez, R. 2006. Prediction in Health Domain Using Bayesian Network Optimization Based on Induction Learning Techniques. International Journal of Modern Physics C 17(3): 447-455.
[39] Cogliati, M., Britos, P. y García-Martínez, R. (2006). Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT. Springer IFIP Series..
[40] Rancán, C. 2004. Arquitectura de Sistema Híbrido de Evaluación del Alistamiento de Unidades Navales Auxiliares. Reportes Técnicos en Ingeniería del Software. 6(1): 45-54.
[41] La Battaglia, J., Rodriguez, I., Thomas, P., Pesado, P., Bertone, R. 2003. Tecnología aplicada a gestión distribuida. Proc. de las XI Jornadas de Jóvenes Investigadores AUGM 2003. Universidad Nacional de La Plata – Argentina.
[42] Miatón, I., Pesado, P., Bertone, R. y De Giusti. 2003. Agentes Basados en Sistemas Distribuidos. Proc. V Workshop de Investigadores en Ciencia de la Computación.
[43] De Giusti E., Mollo Brisco G., La Battaglia, J.,Pasini, A. y Pesado, P. 2004. Sistema de Simulación de Escenarios y Decisiones Empresarias. Proc. XII Jornadas de Jóvenes Investigadores AUGM 2004. Universidad Federal Do Paraná - Curitiba, Brasil.
[44] Pesado, P. Feierherd G. y Pasini, A. 2005. Requirement Specifications for Electronic Voting System. Journal of Computer Science & Technology, 5(4): 312-319 .
[45] Hoffmann, F., Baesens, B., Mues, C. and Vanthienen, J. 2006. Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research. (in press).
[46] Cao, H., Recknagel, F. Joo, G., Kim, D. 2006. Discoveryof predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA. Ecological Informatics, 1(1): 43-53.
[47] Podgorelec, V., Kokol, P., Stiglic, M., Heričko, M., Rozman, I. 2005. Knowledge discovery with classification rules in a cardiovascular dataset. Computer Methods and Programs in Biomedicine, 80: S39-S49.
[48] Huang, M., Tsou, Y., Lee, S. 2006. Integrating fuzzy data mining and fuzzy artificial neural networks fordiscovering implicit knowledge. Knowledge-Based Systems, 19(6): 396-403.
[49] Kasabov, K. 2006. Adaptation and interaction in dynamical systems: Modelling and rule discovery through evolving connectionist systems. Applied Soft Computing, 6(3): 307-322.
[50] Carpenter, G., Martens, S., Ogas, O. 2005. Self-organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks.Neural Networks, 18(3): 287-295.
[51] Liu, D., Ke, C. 2006. Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning. Expert Systems with Applications. (in press).
[52] Moreno, M., Quintales, L., García, F., Polo, J. 2004. Building knowledge discovery-driven models for decision support in project management. Decision Support Systems, 38(2): 305-317.
[53] Kim, M., Han, I. 2003. The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithmsExpert Systems with Applications, 25(4): 637-646.
[54] Leigh, W., Modani, N., Purvis, R., Roberts, R. 2002. Stock market trading rule discovery using technicalcharting heuristics. Expert Systems with Applications, 23(2): 155-159.
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Published
2007-03-01
How to Cite
Rancan, C., Pesado, P. M., & García Martínez, R. (2007). Toward integration of knowledge based systems and knowledge discovery systems. Journal of Computer Science and Technology, 7(01), p. 91–97. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/809
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