Abstract
Markov logic networks (MLNs) have been successfully applied to several challenging problems by taking a “programming language” approach where a set of formulas is hand-coded and weights are learned from data. Because inference plays an important role in this process, “programming” with an MLN would be significantly facilitated by speeding up inference. We present a new meta-inference algorithm that exploits the repeated structure frequently present in relational domains to speed up existing inference techniques. Our approach first clusters the query literals and then performs full inference for only one representative from each cluster. The clustering step incurs only a one-time up-front cost when weights are learned over a fixed structure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107–136 (2006)
Poon, H., Domingos, P.: Joint inference in information extraction. In: Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI 2007), Vancouver, Canada (2007)
Wu, F., Weld, D.: Automatically refining the Wikipedia infobox ontology. In: Proceedings of the Seventeenth International World Wide Web Conference (WWW 2008), Beijing, China (2008)
Lowd, D., Domingos, P.: Efficient weight learning for Markov logic networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 200–211. Springer, Heidelberg (2007)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Singla, P., Domingos, P.: Memory-efficient inference in relational domains. In: Proceedings of the Twenty-First Conference on Artificial Intelligence (AAAI 2006), Boston, MA (2006)
Shavlik, J., Natarajan, S.: Speeding up inference in Markov logic networks by preprocessing to reduce the size of the resulting grounded network. In: Proceedings of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI 2009), Pasadena, CA (2009)
Poon, H., Domingos, P.: Sound and efficient inference with probabilistic and deterministic dependencies. In: Proceedings of the Twenty-First Conference on Artificial Intelligence (AAAI 2006), Boston, MA (2006)
Frasconi, P., Passerini, A.: Learning with kernels and logical representations. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 56–91. Springer, Heidelberg (2008)
Kautz, H., Selman, B., Jiang, Y.: A general stochastic approach to solving problems with hard and soft constraints. In: Gu, D., Du, J., Pardalos, P. (eds.) DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 35, pp. 573–586. American Mathematical Society, Providence (1997)
Kok, S., Singla, P., Richardson, M., Domingos, P.: The Alchemy system for statistical relational AI. Technical report, Department of Computer Science and Engineering, University of Washington (2005), http://www.cs.washington.edu/ai/alchemy
Mihalkova, L., Mooney, R.J.: Bottom-up learning of Markov logic network structure. In: Proceedings of 24th International Conference on Machine Learning (ICML 2007), Corvallis, OR (2007)
Poole, D.: First-order probabilistic inference. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico (2003)
de Salvo Braz, R., Amir, E., Roth, D.: MPE and partial inversion in lifted probabilistic variable elimination. In: Proceedings of the Twenty-First Conference on Artificial Intelligence (AAAI 2006), Boston, Massachusetts (2006)
Milch, B., Zettlemoyer, L.S., Kersting, K., Haimes, M., Kaelbling, L.P.: Lifted probabilistic inference with counting formulas. In: Proceedings of the Twenty-Third Conference on Artificial Intelligence, AAAI 2008 (2008)
Sen, P., Deshpande, A., Getoor, L.: Exploiting shared correlations in probabilistic databases. In: Proceedings of the 34th International Conference on Very Large Data Bases (VLDB 2008), Auckland, New Zealand (2008)
Sen, P., Deshpande, A., Getoor, L.: Bisimulation-based approximate lifted inference. In: Proceedings of 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 (2009)
Jaimovich, A., Meshi, O., Friedman, N.: Template based inference in symmetric relational Markov random fields. In: Proceedings of 23rd Conference on Uncertainty in Artificial Intelligence (UAI 2007), Vancouver, Canada, pp. 191–199 (2007)
Singla, P., Domingos, P.: Lifted first-order belief propagation. In: Proceedings of the Twenty-Third Conference on Artificial Intelligence (AAAI 2008), Chicago, IL (2008)
de Salvo Braz, R., Natarajan, S., Bui, H., Shavlik, J., Russell, S.: Anytime lifted belief propagation. In: International Workshop on Statistical Relational Learning (SRL 2009), Leuven, Belgium (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mihalkova, L., Richardson, M. (2010). Speeding Up Inference in Statistical Relational Learning by Clustering Similar Query Literals. In: De Raedt, L. (eds) Inductive Logic Programming. ILP 2009. Lecture Notes in Computer Science(), vol 5989. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13840-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-13840-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13839-3
Online ISBN: 978-3-642-13840-9
eBook Packages: Computer ScienceComputer Science (R0)