Online structure learning for Markov logic networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6912 LNAI, Issue PART 2, 2011, Pages 81-96

  Huynh, Tuyen N ^a   Mooney, Raymond J ^a  

^a University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LARGE DATASETS; LEARNING METHODS; MAIN MEMORY; MARKOV LOGIC NETWORKS; ON-LINE STRUCTURES; ONLINE LEARNING; PARAMETER LEARNING; REAL-WORLD DATASETS; TRAINING EXAMPLE;

LEARNING SYSTEMS;

E-LEARNING;

EID: 80052418012     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23783-6_6     Document Type: Conference Paper

References (35)

1. Biba, M., Ferilli, S., Esposito, F.: Discriminative structure learning of markov logic networks. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 59-76. Springer, Heidelberg (2008)
2. Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 shared task: Semantic role labeling. In: Proc. of the 9th Conf. on Computational Natural Language Learning (CoNLL 2005), pp. 152-164 (2005)
3. Della Pietra, S., Della Pietra, V.J., Lafferty, J.D.: Inducing features of random fields. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(4), 380-393 (1997)
4. Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. Morgan & Claypool Publishers, San Francisco (2009)
5. Duboc, A.L., Paes, A., Zaverucha, G.: Using the bottom clause and mode declarations on FOL theory revision from examples. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 91-106. Springer, Heidelberg (2008)
6. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. Tech. rep., EECS Department, University of California, Berkeley (2010), http://www.cs.berkeley.edu/Pubs/TechRpts/2010/ EECS-2010-24.html
7. Fei-Fei, L., Li, L.J.: What, Where and Who? Telling the Story of an Image by Activity Classification, Scene Recognition and Object Categorization. In: Computer Vision: Detection, Recognition and Reconstruction, pp. 157-171 (2010)
8. Genesereth, M.R., Nilsson, N.J.: Logical foundations of artificial intelligence. Morgan Kaufmann, San Francisco (1987)
9. Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
10. Grenager, T., Klein, D., Manning, C.D.: Unsupervised learning of field segmentation models for information extraction. In: Proc. of the 43nd Annual Meeting of the Asso. for Computational Linguistics, ACL 2005 (2005)
11. Huynh, T.N., Mooney, R.J.: Online max-margin weight learning with Markov Logic Networks. In: Proc. of the 2011 SIAM Int. Conf. on Data Mining (SDM 2011), pp. 642-651 (2011)
12. Huynh, T.N., Mooney, R.J.: Max-Margin Weight Learning for Markov Logic Networks. In: Buntine,W., Grobelnik,M.,Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 564-579. Springer, Heidelberg (2009)
13. Kok, S., Domingos, P.: Learning the structure of Markov logic networks. In: ICML 2005 (2005)
14. Kok, S., Domingos, P.: Learning Markov logic network structure via hypergraph lifting. In: Proc. of 26th Int. Conf. on Machine Learning (ICML 2009), pp. 505-512 (2009)
15. Kok, S., Domingos, P.: Learning Markov logic networks using structural motifs. In: Proc. of 27th Int. Conf. on Machine Learning (ICML 2010), pp. 551-558 (2010)
16. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. of 18th Int. Conf. on Machine Learning (ICML 2001), pp. 282-289 (2001)
17. Lawrence, S., Giles, C.L., Bollacker, K.D.: Autonomous citation matching. In: Proc. of the 3rd Annual Conf. on Autonomous Agents, pp. 392-393 (1999)
18. 1-regularization. In: Adv. in Neu. Infor. Processing Systems (NIPS 2006), vol. 19, pp. 817-824 (2007)
19. McCallum, A.: Efficiently inducing features of conditional random fields. In: Proc. of 19th Conf. on Uncertainty in Artificial Intelligence (UAI 2003), pp. 403-410 (2003)
20. Mihalkova, L., Huynh, T., Mooney, R.J.: Mapping and revising Markov logic networks for transfer learning. In: Proc. of the 22nd Conf. on Artificial Intelligence (AAAI 2007), pp. 608-614 (2007)
21. Mihalkova, L., Mooney, R.J.: Bottom-up learning of Markov logic network structure. In: Proc. of 24th Int. Conf. on Machine Learning, ICML 2007 (2007)
22. Mihalkova, L., Mooney, R.J.: Learning to disambiguate search queries from short sessions. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 111-127. Springer, Heidelberg (2009)
23. Muggleton, S.: Inverse entailment and Progol. New Generation Computing 13, 245-286 (1995)
24. Ong, I.M., De Castro Dutra, I., Page, D., Costa, V.S.: Mode directed path finding. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 673-681. Springer, Heidelberg (2005)
25. Perkins, S., Theiler, J.: Online feature selection using grafting. In: Proc. of 20th Int. Conf. on Machine Learning (ICML 2003), pp. 592-599 (2003)
26. Poon, H., Domingos, P.: Joint inference in information extraction. In: Proc. of the 22nd Conf. on Artificial Intelligence (AAAI 2007), pp. 913-918 (2007)
27. Richards, B.L., Mooney, R.J.: Learning relations by pathfinding. In: Proc. of the 10th Nat. Conf. on Artificial Intelligence (AAAI 1992), pp. 50-55 (1992)
28. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107-136 (2006)
29. Riedel, S., Meza-Ruiz, I.: Collective semantic role labelling with Markov logic. In: Proc. of the 12th Conf. on Computational Natural Language Learning (CoNLL 2008), pp. 193-197 (2008)
30. Slattery, S., Craven, M.: Combining statistical and relational methods for learning in hypertext domains. In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, pp. 38-52. Springer, Heidelberg (1998) (Pubitemid 128110606)
31. Sutton, C., McCallum, A.: An introduction to conditional random fields for relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning, pp. 93-127. MIT Press, Cambridge (2007)
32. Taskar, B., Guestrin, C., Koller, D.: Max-margin Markov networks. In: Adv. in Neu. Infor. Processing Systems, NIPS 2003, vol. 16 (2004)
33. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: Proc. of 21st Int. Conf. on Machine Learning (ICML 2004), pp. 104-112 (2004)
34. Zelle, J.M., Thompson, C.A., Califf, M.E., Mooney, R.J.: Inducing logic programs without explicit negative examples. In: Swierstra, S.D. (ed.) PLILP 1995. LNCS, vol. 982, pp. 403-416. Springer, Heidelberg (1995)
35. Zhu, J., Lao, N., Xing, E.P.: Grafting-light: fast, incremental feature selection and structure learning of Markov random fields. In: Proc. of the 16th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2010), pp. 303-312 (2010)