Learning graphical models for relational data via lattice search

Machine Learning

Volumn 88, Issue 3, 2012, Pages 331-368

  Schulte, Oliver ^a   Khosravi, Hassan ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Bayes nets; Graphical models; Markov logic networks; Statistical relational learning

Indexed keywords

BAYES NET; COMPLEMENTARY PROBLEMS; FIRST ORDER LOGIC; GRAPHICAL MODEL; MACHINE LEARNING APPLICATIONS; MAIN TASKS; MARKOV LOGIC NETWORKS; NON-BINARY; PREDICTIVE ACCURACY; RELATIONAL DATA; RELATIONAL DATABASE; RICHARDSON; SEARCH SPACES; SMALL DATA SET; STATE-OF-THE-ART ALGORITHMS; STATISTICAL-RELATIONAL LEARNING;

DATABASE SYSTEMS; GRAPHIC METHODS; MARKOV PROCESSES; SPEECH RECOGNITION;

LEARNING ALGORITHMS;

EID: 84865227776     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-012-5289-4     Document Type: Article

References (61)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proc. international conference on very large databases, Santiage, Chile (pp. 478-499). Los Altos: Morgan Kaufmann.
2. Apt, K. R., & Bezem, M. (1991). Acyclic programs. New Generation Computing, 9(3/4), 335-364.
3. Biba,M., Ferilli, S., & Esposito, F. (2008). Structure learning of Markov logic networks through iterated local search. In M. Ghallab, C. D. Spyropoulos, N. Fakotakis, & N. M. Avouris (Eds.), ECAI (pp. 361-365).
4. Bratko, I. (2001). Prolog: Programming for artificial intelligence (3rd ed.). Boston: Addison-Wesley/Longman.
5. Chen, H., Liu, H., Han, J., & Yin, X. (2009). Exploring optimization of semantic relationship graph for multi-relational Bayesian classification. Decision Support Systems, 48(1), 112-121.
6. Chickering, D. (2003). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3, 507-554.
7. CMU The Tetrad Group, Department of Philosophy (2008). The Tetrad project: Causal models and statistical data. http://www.phil.cmu.edu/projects/ tetrad/.
8. Domingos, P., & Richardson, M. (2007). Markov logic: A unifying framework for statistical relational learning. In Introduction to statistical relational learning. Cambridge: MIT Press.
9. Domke, J., Karapurkar, A., & Aloimonos, Y. (2008). Who killed the directed model. In CVPR (pp. 1-8).
10. Fierens, D. (2009). On the relationship between logical Bayesian networks and probabilistic logic programming based on the distribution semantics. In L. De Raedt (Ed.), Lecture notes in computer science: Vol. 5989. ILP (pp. 17-24). Berlin: Springer.
11. Fierens, D., Blockeel, H., Bruynooghe,M., & Ramon, J. (2005). Logical Bayesian networks and their relation to other probabilistic logical models. In S. Kramer & B. Pfahringer (Eds.), Lecture notes in computer science: Vol. 3625. ILP (pp. 121-135). Berlin: Springer. (Pubitemid 41479989)
12. Frank, O. (1977). Estimation of graph totals. Scandinavian Journal of Statistics, 4(2), 81-89.
13. Frank, R., Moser, F., & Ester, M. (2007). A method for multi-relational classification using single and multifeature aggregation functions. In PKDD (pp. 430-437).
14. Friedman, N., Getoor, L., Koller, D., & Pfeffer, A. (1999). Learning probabilistic relational models. In IJCAI (pp. 1300-1309). Berlin: Springer.
15. Geiger, D., & Heckerman, D. (1996). Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence, 82(1-2), 45-74. (Pubitemid 126374148)
16. Getoor, L., & Grant, J. (2006). Prl: A probabilistic relational language. Machine Learning, 62(1-2), 7-31. (Pubitemid 43202305)
17. Getoor, L., & Tasker, B. (2007). Introduction to statistical relational learning. Cambridge: MIT Press.
18. Getoor, L., Friedman, N., Koller, D., Pfeffer, A., & Taskar, B. (2007). Probabilistic relational models. In Introduction to statistical relational learning (pp. 129-173). Cambridge: MIT Press, Chap. 5.
19. Heckerman, D., Meek, C., & Koller, D. (2007). Probabilistic entity-relationship models, PRMs, and plate models. In Introduction to statistical relational learning. Cambridge: MIT Press.
20. Huynh, T. N., & Mooney, R. J. (2008). Discriminative structure and parameter learning for Markov logic networks. In W. W. Cohen, A. McCallum, & S. T. Roweis (Eds.), ICML (pp. 416-423). New York: ACM.
21. Jensen, D., & Neville, J. (2002). Linkage and autocorrelation cause feature selection bias in relational learning. In ICML.
22. Kersting, K., & de Raedt, L. (2007). Bayesian logic programming: Theory and tool. In Introduction to statistical relational learning (pp. 291-318). Cambridge: MIT Press, Chap. 10.
23. Khosravi, H., Schulte, O., & Bina, B. (2009). Virtual joins with nonexistent links. In 19th conference on inductive logic programming (ILP). http://www.cs.kuleuven.be/~dtai/ilp-mlg-srl/papers/ILP09-39.pdf.
24. Khosravi, H., Schulte, O., Man, T., Xu, X., & Bina, B. (2010). Structure learning for Markov logic networks with many descriptive attributes. In Proceedings of the twenty-fourth conference on artificial intelligence (AAAI) (pp. 487-493).
25. Klug, A. C. (1982). Equivalence of relational algebra and relational calculus query languages having aggregate functions. Journal of the ACM, 29(3), 699-717.
26. Kok, S., & Domingos, P. (2005). Learning the structure of Markov logic networks. In L. De Raedt & S. Wrobel (Eds.), ICML (pp. 441-448). New York: ACM.
27. Kok, S., & Domingos, P. (2007). Statistical predicate invention. In ICML (pp. 433-440). New York: ACM.
28. Kok, S., & Domingos, P. (2009). Learning Markov logic network structure via hypergraph lifting. In A. Pohoreckyj Danyluk, L. Bottou, & M. L. Littman (Eds.), ICML (pp. 64-71). New York: ACM.
29. Kok, S., & Domingos, P. (2010). Learning Markov logic networks using structural motifs. In ICML 10 (pp. 551-558).
30. Kok, S., Summer, M., Richardson, M., Singla, P., Poon, H., Lowd, D., Wang, J., & Domingos, P. (2009). The alchemy system for statistical relational AI (Technical Report). University of Washington.
31. Koller, D., & Pfeffer, A. (1997). Learning probabilities for noisy first-order rules. In IJCAI (pp. 1316-1323). Learn and join algorithm code. http://www.cs.sfu.ca/~oschulte/jbn/.
32. Lifschitz, V. (1996). Foundations of logic programming. In Principles of knowledge representation. Stanford: CSLI.
33. Lodhi, H., &Muggleton, S. (2005). Is mutagenesis still challenging? In Inductive logic programming (pp. 35, 40).
34. Lowd, D., & Domingos, P. (2007). Efficient weight learning for Markov logic networks. In PKDD (pp. 200-211).
35. May, W. (1999). Information extraction and integration: The mondial case study (Technical Report). Universitat Freiburg, Institut für Informatik.
36. Mihalkova, L., & Mooney, R. J. (2007). Bottom-up learning of Markov logic network structure. In ICML (pp. 625-632). New York: ACM.
37. Natarajan, S., Tadepalli, P., Dietterich, T.G., & Fern, A. (2008). Learning first-order probabilistic models with combining rules. Annals of Mathematics and Artificial Intelligence, 54(1-3), 223-256.
38. Neville, J., & Jensen, D. (2007). Relational dependency networks. In Introduction to statistical relational learning. Cambridge: MIT Press, Chap. 8.
39. Neville, J., & Jensen, D. (2007). Relational dependency networks. Journal of Machine Learning Research, 8, 653-692. (Pubitemid 46491656)
40. Ngo, L., & Haddawy, P. (1997). Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171(1-2), 147-177. (Pubitemid 127452811)
41. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo: Morgan Kaufmann.
42. Poole, D. (2003). First-order probabilistic inference. In G. Gottlob & T. Walsh (Eds.), IJCAI (pp. 985-991). San Mateo: Morgan Kaufmann.
43. Poon, H., & Domingos, P. (2006). Sound and efficient inference with probabilistic and deterministic dependencies. In AAAI. Menlo Park: AAAI Press.
44. Popescul, A., & Ungar, L. (2007). Feature generation and selection in multi-relational learning. In An introduction to statistical relational learning. Cambridge: MIT Press, Chap. 8.
45. Quinlan, J. (1996). Boosting first-order learning. In Algorithmic learning theory (pp. 143-155). Berlin: Springer.
46. Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach. New York: Prentice Hall.
47. Schmidt, M., Murphy, K., Fung, G., & Rosales, R. (2008). Structure learning in random fields for heart motion abnormality detection. In CVPR.
48. Schulte, O. (2011). A tractable pseudo-likelihood function for Bayes Nets applied to relational datasets. In SIAM SDM (pp. 462-473).
49. Schulte, O., Khosravi, H., & Bina, B. (2009). Bayes nets for combining logical and probabilistic structure. In Proceedings STRUCK workshop on learning structural knowledge from observations. IJCAI-09.
50. Schulte, O., Khosravi, H., Man, T., & Gao, T. (2011). Learning directed relational models with recursive dependencies. In Inductive logic programming.
51. Sebag, M., & Rouveirol, C. (1997). Tractable induction and classification in first order logic via stochastic matching.
52. She, R., Wang, K., & Xu, Y. (2005). Pushing feature selection ahead of join.
53. Srinivasan, A., Muggleton, S. H., Sternberg, M. J. E., & King, R. D. (1996). Theories for mutagenicity: A study in first-order and feature-based induction. Artificial Intelligence, 85(1-2), 277-299. (Pubitemid 126363732)
54. Taskar, B., Abbeel, P., & Koller, D. (2002). Discriminative probabilistic models for relational data. In A. Darwiche & N. Friedman (Eds.), UAI (pp. 485-492). San Mateo: Morgan Kaufmann.
55. Tillman, R. E., Danks, D., & Glymour, C. (2008). Integrating locally learned causal structures with overlapping variables. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), NIPS (pp. 1665-1672). Cambridge: MIT Press.
56. Ullman, J. D. (1982). Principles of database systems. New York: Comput. Sci. Press.
57. Van Laer, W., & de Raedt, L. (2001). How to upgrade propositional learners to first-order logic: A case study. In Relational data mining. Berlin: Springer.
58. Vardi, M. Y. (1995). On the complexity of bounded-variable queries. In PODS (pp. 266-276). New York: ACM.
59. Wellman, M. P., Breese, J. S., & Goldman, R. P. (1992). From knowledge bases to decision models. Knowledge Engineering Review, 7, 35-53.
60. Yin, X., & Han, J. (2008). Exploring the power of heuristics and links in multi-relational data mining. In ISMIS' 08: Proceedings of the 17th international conference on foundations of intelligent systems (pp. 17-27). Berlin: Springer. (Pubitemid 351775874)
61. Yin, X., Han, J., Yang, J., & Yu, P. S. (2004). Crossmine: Efficient classification across multiple database relations. In Constraint-based mining and inductive databases (pp. 172-195). (Pubitemid 43900824)