Intelligent systems using Web-pages as knowledge base for statistical decision making
Tóm tắt
In this paper, we propose an approach to the construction of an intelligent system that handles various domain information provided on the Internet. The intelligent system adopts statistical decision-making as its reasoning framework and automatically constructs probabilistic knowledge, required for its decision-making, from Web-pages. This construction of probabilistic knowledge is carried out using aprobability interpretation idea that transforms statements in Web-pages into constraints on the subjective probabilities of a person who describes the statements. In this paper, we particularly focus on describing the basic idea of our approach and on discussing difficulties in our approach, including our perspective.
Tài liệu tham khảo
Agrawal, R., Imielinski, T. and Swami, A., “Database Mining: A Performance Perspective,”IEEE Transactions on Knowledge and Data Engineering, 5, 6, pp. 914–925, 1993.
Berger, J. O.,Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, 1985.
Beyth-Marom, R., “How Probable is Probable? A Numerical Translation of Verbal Probability Expressions,”Journal of Forecasting, 1, pp. 257–269, 1982.
Druzdzel, M., “Verbal Uncertainty Expressions: Literature Review,”CMU-EPP-1900-03-02, Carnegie Mellon University, 1989.
Druzdzel, M. and van der Gaag, L., “Elicitation of Probabilities for Belief Networkds: Combining Qualitative and Quantitative Information,” inProc. of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-95), Morgan Kaufmann, pp. 141–148, 1995.
Goldszmidt, M. and Pearl, J., “Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling,”Artificial Intelligence, 84, 1, pp. 57–112, 1996.
Heckerman, D. and Jimison, H., “A Bayesian Perspective on Confidence,” inUncertainty in Artificial Intelligence 3 (Kanal, L. N., Levitt, T. S., and Lemmer, J. F. ed.), Elsevier Science Publisher, pp. 149–160, 1989.
Knoblock, C. and Levy, A. ed., Information Gathering from Heterogeneous, Distributed Environments,AAAI Spring Symposium Series Technical Reports, The AAAI Press, 1995.
Lauritzen, S. L. and Spiegelhalter, D. J., “Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems,”Journal of the Royal Statistical Society, 50, 2, pp. 157–224, 1988.
Mahech, K. ed., Natural Language Proc. for the World Wide Web,AAAI Spring Symposium Series Technical Reports, The AAAI Press, 1997.
Paaß, G., “Second Order Probabilities for Uncertain and Conflicting Evidence,” inUncertainty in Artificial Intelligence 6 (Bonissone, P. P., Henrion, M., Kanal, L. N. and Lemmer, J. F. ed.), Elsevier Science Publisher, pp. 447–456, 1991.
Pearl, J.,Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
Piatetsky-Shapiro, G., “Data Mining and Knowledge Discovery References,” URL: http://www.kdnuggets.com/references.html.
Ramoni, M. and Riva, A., “Belief Maintenance in Bayesian Networks,” inProc. of the Tenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-94), Morgan Kaufmann, pp. 498–505, 1994.
Ramoni, M. and Sebastiani, P., “Learning Bayesian Networks from Incomplete Databases,” inProc. of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), Morgan Kaufmann, pp. 401–408, 1997.
Winterfeldt, D. V. and Edwards, W.,Decision Analysis and Behavioral Research, Cambridge University Press, 1986.