Structure learning for Markov Logic Networks with many descriptive attributes

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2010, Pages 487-493

  Khosravi, Hassan ^a   Schulte, Oliver ^a   Man, Tong ^a   Xu, Xiaoyuan ^a   Bina, Bahareh ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER CIRCUITS; LEARNING SYSTEMS; MARKOV PROCESSES; PROBABILISTIC LOGICS;

COMPLEMENTARY PROBLEMS; FIRST ORDER LOGIC; MACHINE LEARNING APPLICATIONS; MARKOV LOGIC NETWORKS; PREDICTIVE ACCURACY; RELATIONAL DATABASE; STATE-OF-THE-ART ALGORITHMS; STRUCTURE-LEARNING;

LEARNING ALGORITHMS;

EID: 77958615695     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Bacchus, F. 1990. Representing and reasoning with probabilistic knowledge: a logical approach to probabilities. Cambridge, MA, USA: MIT Press.
2. Biba, M.; Fertili, S.; and Esposito, F. 2008. Structure learning of Markov logic networks through iterated local search. In Ghallab, M.; Spyropoulos, C. D.; Fakotakis, N.; and Avouris, N. M., eds., ECAI, 361-365.
3. Chickering, D. 2003. Optimal structure identification with greedy search. Journal of Machine Learning Research 3:507-554.
4. Domingos, P., and Richardson, M. 2007. Markov logic: A unifying framework for statistical relational learning. In Introduction to Statistical Relational Learning (2007).
5. Getoor, L., and Tasker, B. 2007. Introduction to statistical relational learning. MIT Press.
6. Getoor, L.; Friedman, N.; Koller, D.; Pfeffer, A.; and Taskar, B. 2007. Probabilistic relational models. In Introduction to Statistical Relational Learning (2007). chapter 5, 129-173.
7. Getoor, L.; Taskar, B.; and Koller, D. 2001. Selectivity estimation using probabilistic models. ACM SIGMOD Record 30(2)461-172.
8. Halpern, J. Y. 1990. An analysis of first-order logics of probability. Artificial Intelligence 46(3):311-350.
9. Huynh, T. N., and Mooney, R. J. 2008. Discriminative structure and parameter learning for markov logic networks. In Cohen, W. W.; McCallum, A.; and Roweis, S. T., eds., ICML, 416-423. ACM.
10. Kersting, K., and de Raedt, L. 2007. Bayesian logic programming: Theory and tool. In Introduction to Statistical Relational Learning (2007). chapter 10, 291-318.
11. Kok, S., and Domingos, P. 2005. Learning the structure of Markov logic networks. In Raedt, L. D., and Wrobel, S., eds., ICML, 441-448. ACM. (Pubitemid 43183364)
12. Kok, S., and Domingos, P. 2007. Statistical predicate invention. In ICML, 433-440. ACM.
13. Kok, S., and Domingos, P. 2009. Learning markov logic network structure via hypergraph lifting. In Danyluk, A. P.; Bottou, L.; and Littman, M. L., eds., ICML, 64-71. ACM.
14. Kok, S.; Summer, M.; Richardson, M.; Singla, P.; Poon, H.; Lowd, D.; Wang, J.; and Domingos, P. 2009. The Alchemy system for statistical relational AI. Technical report, University of Washington.
15. Mihalkova, L., and Mooney, R. J. 2007. Bottom-up learning of Markov logic network structure. In ICML, 625-632. ACM.
16. Neville, J., and Jensen, D. 2007. Relational dependency networks. In An Introduction to Statistical Relational Learning (2007). chapter 8.
17. Poole, D. 2003. First-order probabilistic inference. In Gott-lob, G., and Walsh, T., eds., IJCAI, 985-991. Morgan Kauf-mann.
18. Poon, H., and Domingos, P. 2006. Sound and efficient inference with probabilistic and deterministic dependencies. In AAAI. AAAI Press.
19. Schmidt, M.; Murphy, K.; Fung, G.; and Rosales, R. 2008. Structure learning in random fields for heart motion abnormality detection. In CVPR. IEEE Computer Society.
20. Srinivasan, A.; Muggleton, S.; Sternberg, M.; and King, R. 1996. Theories for mutagenicity: A study in first-order and feature-based induction. Artificial Intelligence 85(l-2):277-299.
21. Taskar, B.; Abbeel, P.; and Koller, D. 2002. Discriminative probabilistic models for relational data. In Darwiche, A., and Friedman, N., eds., UAI, 485-492. Morgan Kaufmann.
22. The Tetrad Group, Department of Philosophy, C. 2008. The Tetrad project: Causal models and statistical data. http://www.phil.cmu.edu/projects/tetrad/.
23. Tillman, R. E.; Danks, D.; and Glymour, C. 2008. Integrating locally learned causal structures with overlapping variables. In Koller, D.; Schuurmans, D.; Bengio, Y; and Bottou, L., eds., NIPS, 1665-1672. MIT Press.
24. Ullman, J. D. 1982. Principles of database systems. 2. Computer Science Press.