Abstract
Probabilistic relational models (PRMs) are a language for describing statistical models over typed relational domains. A PRM models the uncertainty over the attributes of objects in the domain and uncertainty over the relations between the objects. The model specifies, for each attribute of an object, its (probabilistic) dependence on other attributes of that object and on attributes of related objects. The dependence model is defined at the level of classes of objects. The class dependence model is instantiated for any object in the class, as appropriate to the particular context of the object (i.e., the relations between this objects and others). PRMs can also represent uncertainty over the relational structure itself, e.g., by specifying a (class-level) probability that two objects will be related to each other. PRMs provide a foundation for dealing with the noise and uncertainty encountered in most real-world domains. In this chapter, we show that the compact and natural representation of PRMs allows them to be learned directly from an existing relational database using well-founded statistical techniques. We give an introduction to PRMs and an overview of methods for learning them. We show that PRMs provide a new framework for relational data mining, and offer new challenges for the endeavor of learning relational models for real-world domains.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M.A. Behr, M.A. Wilson, W.P. Gill, H. Salamon, G.K. Schoolnik, S. Rane, and P.M. Small. Comparative genomics of BCG vaccines by whole genome DNA microaxray. Science, 284:1520–1523, 1999.
J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 43–52. Morgan Kaufman, San Francisco, CA, 1998.
D. M. Chickering. Learning Bayesian networks is NP-complete. In D. Fisher and H.-J. Lenz, editors, Learning from Data: Artificial Intelligence and Statistics V, pages 121–130. Springer, Berlin, 1996.
G. F. Cooper. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 42: 393–405, 1990.
G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9: 309–347, 1992.
J. Cussens. Loglinear models for first-order probabilistic reasoning. In Pro-ceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 126–133. Morgan Kaufman, San Francisco, CA, 1999.
N. Friedman. Learning belief networks in the presence of missing values and hidden variables. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 125–133. Morgan Kaufman, San Francisco, CA, 1997.
N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1300–1307. Morgan Kaufman, San Francisco, CA, 1999.
L. Getoor, D. Koller, and N. Friedman. From instances to classes in probabilistic relational models. In Proceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries, pages 25–34. Stanford University, Stanford, CA, 2000.
L. Getoor, D. Koller, B. Taskar, and N. Friedman. Learning probabilistic relational models with structural uncertainty. In Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pages 13–20. Technical Report WS-00–06, AAAI Press, Menlo Park, CA, 2000.
D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan, editor, Learning in Graphical Models, pages 301–354. MIT Press, Cambridge, MA, 1998.
T. Hofmann, J. Puzicha, and M. Jordan. Learning from dyadic data. In Advances in Neural Information Processing Systems 11, pages 466–472. MIT Press, Cambridge, MA, 1998.
K. Kersting, L. de Raedt, and S. Kramer. Interpreting Bayesian logic programs. In Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pages 29–35. Technical Report WS-00–06, AAAI Press, Menlo Park, CA, 2000.
D. Koller and A. Pfeffer. Learning probabilities for noisy first-order rules. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1316–1321. Morgan Kaufman, San Francisco, CA, 1997.
D. Koller and A. Pfeffer. Probabilistic frame-based systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 580–587. AAAI Press, Menlo Park, CA, 1998.
S. L. Lauritzen. The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis, 19: 191–201, 1995.
N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, 1994. Freely available at http ://www-ai.ijs.si/Saso/Dzeroski/ILPBook/.
S.H. Muggleton. Stochastic logic programs. In L. de Raedt, editor, Advances in Inductive Logic Programming, pages 254–264. IOS Press, Amsterdam, 1996.
S.H. Muggleton. Learning stochastic logic programs. In Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pages 36–41. Technical Report WS-00–06, AAAI Press, Menlo Park, CA, 2000.
L. Ngo and P. Haddawy. Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171:147–177, 1996.
J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA, 1988.
A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis. Stan-ford University, Stanford, CA, 2000.
D. Poole. Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64:81–129, 1993.
P. Spirtes, C. Glymour, and R. Schemes. Causation, Prediction and Search. Springer, New York, 1993.
M.P. Wellman, J.S. Breese, and R.P. Goldman. From knowledge bases to decision models. The Knowledge Engineering Review, 7(1): 35–53, 1992.
M. Wilson, J.D. DeRisi, H.H. Kristensen, P. Imboden, S. Rane, P.O. Brown, and G.K. Schoolnik. Exploring drug-induced alterations in gene expression in Mycobacterium tuberculosis by microarray hybridization. In Proceedings of the National Academy of Sciences, 96(22):12833–12838, 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Getoor, L., Friedman, N., Koller, D., Pfeffer, A. (2001). Learning Probabilistic Relational Models. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-04599-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07604-6
Online ISBN: 978-3-662-04599-2
eBook Packages: Springer Book Archive