ORION – Um Framework para Refinamento de Ontologias Através de Técnicas de Revisão de Teorias
Resumo
A utilização de Ontologias para representação do conhecimento sobre um domínio requer um trabalho contínuo de manutenção, uma vez que a observação de novas instâncias pode trazer inconsistências às regras previamente definidas. No entanto, a modificação manual da Ontologia pode ser muito custosa e complexa. Este trabalho propõe o ORION, um framework para refinamento automático de Ontologias através do uso de técnicas de revisão de teorias. Testes preliminares mostraram a viabilidade da proposta e a obtenção de uma Ontologia refinada mais precisa.Referências
Buntine, W. (1991). “Theory refinement on Bayesian networks”. In Proceedings 17th Conference Uncertainty in Artificial Intelligence, pages 52–60, San Mateo, CA.
De Raedt, L., Kimmig, A., and Toivonen, H. (2007). “Problog: A probabilistic prolog and its application in link discovery”. In Proc. IJCAI-2007, pp 2462–2467.
Garcez, A. e Zaverucha, G. (1999). “The connectionist inductive learning and logic programming system”. Applied Intelligence, 11:59–77.
Gruber, T. (1995). “Toward principles for the design of ontologies used for knowledge sharing”. International Journal of Human-Computer Studies, 43:907-928.
Han, J.; Kamber, M. (2006). Data Mining: Concepts and Techniques, Morgan Kaufmann Pubs Kang, S. and Lau, S. (2007) Ontology Revision: An Application of Belief Revision Approach, in Sharman, R. et al (eds.) Ontologies: A Handbook of Principles, Concepts and Applications in Information Systems, Springer, pp. 297-318.
Kersting, K. and De Raedt, L. (2001). “Towards combining inductive logic programming with Bayesian networks”. In Proceedings of the 12th Int. Conference on Inductive Logic Programming, LNAI 2157 Springer Verlag, Strasbourg, 118–131.
Klyne, G. e Carroll, J. (2004). “Resource description framework (rdf): W3C recommendation”. available at: [link].
Leão, F., Revoredo, K., Baião, F. (2010) “Ontology Refinement through Theory Revision Techniques”, In III Workshop on Computational Intelligence, Brazil.
Mitchell, T. (1997). “Machine Learning’. McGraw-Hill, New York.
Muggleton, S. (1992). “Inductive logic programming”. Academic Press, New York.
Paes, A., Revoredo, K., Zaverucha, G., Costa, V. S. (2006). “Pforte: Revising probabilistic fol theories”. Proc 18th Brazilian AI Symposium (SBIA-06), LNAI 4140, pp 441–450. Springer.
Poole, D. (1993). “Probabilistic Horn abduction and Bayesian networks”. Artificial Intelligence, 64(1):81–129.
Ramachandran, S. e Mooney, R. (1998). “Theory refinement of bayesian networks with hidden variables”. Proc 15th Intl Conference on Machine Learning (ICML), pp 454–462.
Richards, B. L. and Mooney, R. J. (1995). “Automated refinement of first-order Hornclause domain theories. Machine Learning”, 19:95–131.
Towell, G. e Shavlik, J. (1994). Knowledgebased artificial neural networks. Artificial Intelligence, 70(1–2):119–165.
Wogulis, J. e Pazzani, M. (1993). “A methodology for evaluationg theory revision systems: results with Audrey II”. In Proceedings of the 13th International Join Conference on Artificial Intelligence, pages 1128–1134, Chambery, France.
Wrobel, S. (1996). “First-order theory refinement”. In Raedt, L. D., editor, Advances in Inductive Logic Programming, pages 14–33. IOS Press.
Sun, Y., Sui, Y., Zhiping, L. (2006). “One Axiomatic System for the Ontology Revision”. Intelligent Information Processing III: 91-100
De Raedt, L., Kimmig, A., and Toivonen, H. (2007). “Problog: A probabilistic prolog and its application in link discovery”. In Proc. IJCAI-2007, pp 2462–2467.
Garcez, A. e Zaverucha, G. (1999). “The connectionist inductive learning and logic programming system”. Applied Intelligence, 11:59–77.
Gruber, T. (1995). “Toward principles for the design of ontologies used for knowledge sharing”. International Journal of Human-Computer Studies, 43:907-928.
Han, J.; Kamber, M. (2006). Data Mining: Concepts and Techniques, Morgan Kaufmann Pubs Kang, S. and Lau, S. (2007) Ontology Revision: An Application of Belief Revision Approach, in Sharman, R. et al (eds.) Ontologies: A Handbook of Principles, Concepts and Applications in Information Systems, Springer, pp. 297-318.
Kersting, K. and De Raedt, L. (2001). “Towards combining inductive logic programming with Bayesian networks”. In Proceedings of the 12th Int. Conference on Inductive Logic Programming, LNAI 2157 Springer Verlag, Strasbourg, 118–131.
Klyne, G. e Carroll, J. (2004). “Resource description framework (rdf): W3C recommendation”. available at: [link].
Leão, F., Revoredo, K., Baião, F. (2010) “Ontology Refinement through Theory Revision Techniques”, In III Workshop on Computational Intelligence, Brazil.
Mitchell, T. (1997). “Machine Learning’. McGraw-Hill, New York.
Muggleton, S. (1992). “Inductive logic programming”. Academic Press, New York.
Paes, A., Revoredo, K., Zaverucha, G., Costa, V. S. (2006). “Pforte: Revising probabilistic fol theories”. Proc 18th Brazilian AI Symposium (SBIA-06), LNAI 4140, pp 441–450. Springer.
Poole, D. (1993). “Probabilistic Horn abduction and Bayesian networks”. Artificial Intelligence, 64(1):81–129.
Ramachandran, S. e Mooney, R. (1998). “Theory refinement of bayesian networks with hidden variables”. Proc 15th Intl Conference on Machine Learning (ICML), pp 454–462.
Richards, B. L. and Mooney, R. J. (1995). “Automated refinement of first-order Hornclause domain theories. Machine Learning”, 19:95–131.
Towell, G. e Shavlik, J. (1994). Knowledgebased artificial neural networks. Artificial Intelligence, 70(1–2):119–165.
Wogulis, J. e Pazzani, M. (1993). “A methodology for evaluationg theory revision systems: results with Audrey II”. In Proceedings of the 13th International Join Conference on Artificial Intelligence, pages 1128–1134, Chambery, France.
Wrobel, S. (1996). “First-order theory refinement”. In Raedt, L. D., editor, Advances in Inductive Logic Programming, pages 14–33. IOS Press.
Sun, Y., Sui, Y., Zhiping, L. (2006). “One Axiomatic System for the Ontology Revision”. Intelligent Information Processing III: 91-100
Publicado
19/07/2011
Como Citar
LEÃO, Felipe; REVOREDO, Kate; BAIÃO, Fernanda.
ORION – Um Framework para Refinamento de Ontologias Através de Técnicas de Revisão de Teorias. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 8. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 821-832.
ISSN 2763-9061.
