Modelling and searching of combinatorial spaces based on markov logic networks

Journal of Algorithms and Computational Technology

Volumn 5, Issue 2, 2011, Pages 289-312

  Biba, Marenglen ^a   Ferilli, Stefano ^b   Esposito, Floriana ^b  

^a University of Durres Albania   (Albania)

^b UNIVERSITY OF BARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SEARCH; CONDITIONAL LIKELIHOOD; DATA SETS; FIRST ORDER LOGIC; FIRST-ORDER FORMULAS; ITERATED LOCAL SEARCH; MARKOV LOGIC NETWORKS; MARKOV NETWORKS; OBJECTIVE FUNCTIONS; PREDICTION TASKS; QUERY PREDICATES; RANDOMIZED SEARCH; REAL-WORLD DATASETS; RELATIONAL DATABASE; SEARCHING STRATEGY; SMALL DATA SET; STATE-OF-THE-ART METHODS;

ALGORITHMS; OPTIMIZATION;

MAXIMUM LIKELIHOOD;

EID: 79955585791     PISSN: 17483018     EISSN: 17483026     Source Type: Journal    
DOI: 10.1260/1748-3018.5.2.289     Document Type: Conference Paper

References (43)

1. Halpern, J.: An analysis of first-order logics of probability. Artificial Intelligence 46 (1990) 311-350
2. Nilsson, N.: Probabilistic logic. Artificial Intelligence 28 (1986) 71-87
3. Wellman, M. Breese, J.S., Goldman, R.P.: From knowledge bases to decision models. Knowledge Engineering Review 7 (1992)
4. Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning probabilistic models of link structure. Journal of Machine Learning Research 3 (2002) 679-707
5. Natarajan, S., Tadepalli, P., Altendorf, E., Dietterich, T.G., Fern, A., Res-tificar, A.C.: Learning first-order probabilistic models with combining rules. In: ICML. (2005) 609-616 (Pubitemid 43183384)
6. Neville, J., Jensen, D.: Dependency networks for relational data. In: Proc. 4th IEEE Int'l Conf. on Data Mining, IEEE Computer Society Press. (2004) 170-177 (Pubitemid 40731028)
7. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62 (2006)107-236
8. Singla, P., Domingos, P.: Markov logic in infinite domains. In: Proc. 23rd UAI, AUAI Press (2007) 368-375
9. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26 (1997) 99-146 (Pubitemid 127507163)
10. Besag, J.: Statistical analysis of non-lattice data. Statistician 24 (1975) 179-195
11. Kok, S., Domingos, P.: Learning the structure of markov logic networks. In: Proc. 22nd Int'l Conf. on Machine Learning. (2005) 441-448 (Pubitemid 43183364)
12. Mihalkova, L., Mooney, R.J.: Bottom-up learning of markov logic network structure. In: Proc. 24th Int'l Conf. on Machine Learning. (2007) 625-632
13. Biba, M., Ferilli, S., Esposito, F.: Structure learning of markov logic networks through iterated local search. In: Proceedings of 18th European Conf. on AI (ECAI-08). (2008) 361-365
14. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th ICML. (2001) 282-289
15. Singla, P., Domingos, P.: Discriminative training of markov logic networks. In: Proc. 20th Nat'l Conf. on AI, (AAAI), AAAI Press (2005) 868-873 (Pubitemid 43006718)
16. Lowd, D., Domingos, P.: Efficient weight learning for markov logic networks. In: Proc. 11th PKDD, Springer (2007) 200-211
17. Loureno, H., Martin, O., Stutzle, T.: Iterated local search. In: Handbook of Metaheuristics. F. Glover and G. Kochenberger, Kluwer, Norwell, MA, USA (2002) 321-353
18. Hoos, H.H., Stutzle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2005)
19. Feo, T.A., Resende, M.: A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8(2) (1989) 67-71
20. Feo, T.A., Resende, M.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6 (1995) 109-133
21. Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and applications. UK: Ellis Horwood, Chichester (1994)
22. De Raedt, L.: Logical settings for concept-learning. Artificial Intelligence 95(1) (1997) 197-201
23. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5 (1990) 239-266
24. Furnkranz, J.: Separate-and-conquer rule learning. Artificial Intelligence Review 13(1) (1999) 3-54
25. Nienhuys-Cheng, S.H., de Wolf, R.: Foundations of Inductive Logic Programming. Springer-Verlag (1997)
26. Shapiro., E.: Algorithmic Program Debugging. MIT Press (1983)
27. Plotkin., G.D.: A note on inductive generalization. In Machine Intelligence, Edinburgh University Press 5 (1970) 153-163
28. Delia Pietra, S., Pietra, V.D., Laferty, J.: Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 380-392
29. Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proc. HLTNAACL- 03. (2003) 134-141
30. McCallum, A.: Efficiently inducing features of conditional random fields. In: Proc. UAI-03. (2003) 403-410
31. Liu, D.C., Nocedal, J.: On the limited memory bfgs method for large scale optimization. Mathematical Programming 45 (1989) 503-528 (Pubitemid 20660315)
32. Huynh, T.N., Mooney, R.J.: Discriminative structure and parameter learning for markov logic networks. In: In Proc. of the 25th International Conference on Machine Learning (ICML). (2008)
33. Grossman, D., Domingos, P.: Learning bayesian network classifiers by maximizing conditional likelihood. In: Proc. 21st ICML, ACM Press (2004) 361-368 (Pubitemid 40290829)
34. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proc. 23rd ICML. (2006) 233-240
35. Singla, P., Domingos, P.: Entity resolution with markov logic. In: Proc. ICDM-06. (2006) 572-582 (Pubitemid 47485836)
36. Kok, S., Singla, P., Richardson, M., Domingos, P.: The alchemy system for statistical relational ai. Technical report, Department of CSE-UW, Seattle, WA, http://alchemy. cs.washington.edu/ (2005)
37. Dehaspe, L.: Maximum entropy modeling with clausal constraints. In: Proc. of ILP- 97, Springer (1997) 109-124 (Pubitemid 127124382)
38. Popescul, A., Ungar, L.H.: Structural logistic regression for link analysis. In: Proc. of MRDM-03, Washington, DC: ACM Press (2003) 92-106
39. Landwehr, N., Kersting, K., De Raedt, L.: nfoil: Integrating naive bayes and foil. In: Proc. 20th Nat'l Conf. on Artificial Intelligence, AAAI Press (2005) 795-800 (Pubitemid 43006706)
40. Davis, J., E. Burnside, I.d.C.D., Page, D., Costa, V.S.: An integrated approach to learning bayesian networks of rules. In: Proc. ECML-2005. Volume 3720 LNCS. (2005) 84-95
41. Landwehr, N., Passerini, A., L., D.R., Prasconi, P.: kfoil: Learning simple relational kernels. In: Proc. 21st Nat'l Conf. on Artificial Intelligence, AAAI Press (2006)
42. Landwehr, N., Kersting, K., De Raedt, L.: Integrating naive bayes and foil. J. of Machine Learning Research (2007) 481-507
43. Zelezny, F., Srinivasan., A., Page., D.: Randomised restarted search in ilp. Machine Learning 64(1-3) (2006) 183-208 (Pubitemid 44320251)