ABSTRACT
Statistical Relational Learning (SRL) is a subarea of machine learning which combines elements from statistical and probabilistic modeling with languages which support structured data representations. In this survey, we will: 1) provide an introduction to SRL, 2) describe some of the distinguishing characteristics of SRL systems, including relational feature construction and collective classification, 3) describe three SRL systems in detail, 4) discuss applications of SRL techniques to important data management problems such as entity resolution, selectivity estimation, and information integration, and 5) discuss connections between SRL methods and existing database research such as probabilistic databases.
Supplemental Material
- C. Aggarwal and H. Wang, editors. Managing and Mining Graph Data. Springer, 2010. Google ScholarDigital Library
- G. Bakir, T. Hofmann, B. Schölkopf, A. Smola, B. Taskar, and S. Vishwanathan. Predicting Structured Data. MIT Press, 2007. Google ScholarDigital Library
- M. Broecheler, L. Mihalkova, and L. Getoor. Probabilistic similarity logic. In UAI, 2010.Google Scholar
- L. De Raedt. Logical and relational learning, 2009. IJCAI Tutorial.Google Scholar
- L. De Raedt and K. Kersting. Probabilistic inductive logic programming, 2005. ECML/PKDD Tutorial.Google Scholar
- R. de Salvo Braz, E. Amir, and D. Roth. Lifted first-order probabilistic inference. In IJCAI, 2005. Google ScholarDigital Library
- R. de Salvo Braz, E. Amir, and D. Roth. MPE and partial inversion in lifted probabilistic variable elimination. In AAAI, 2006. Google ScholarDigital Library
- R. de Salvo Braz, S. Natarajan, H. Bui, J. Shavlik, and S. Russell. Anytime lifted belief propagation. In SRL, 2009.Google Scholar
- T. Dietterich, L. Getoor, and K. Murphy, editors. SRL2004: Statistical Relational Learning and its Connections to Other Fields, Banff, Alberta, Canada, 2004.Google Scholar
- P. Domingos. Practical statistical relational learning, 2007. ICML Tutorial.Google Scholar
- P. Domingos and K. Kersting, editors. International Workshop on Statistical Relational Learning (SRL-2009), Leuven, Belgium, 2009.Google Scholar
- P. Domingos and D. Lowd. Markov Logic: An Interface Layer for Artificial Intelligence. Morgan&Claypool, 2009. Google ScholarDigital Library
- S. Dzeroski, L. De Raedt, and S. Wrobel, editors. Second International Workshop on Multi-Relational Data Mining, Washington, DC, 2003.Google Scholar
- A. Fern, L. Getoor, and B. Milch, editors. SRL2006: Open Problems in Statistical Relational Learning at ICML 2006, Pittsburgh, PA, 2006.Google Scholar
- N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI, 1999. Google ScholarDigital Library
- L. Getoor. Statistical relational learning, 2006. ICML/ILP Tutorial. Google ScholarDigital Library
- L. Getoor. Statistical relational learning, 2007. ECML Tutorial.Google Scholar
- L. Getoor, N. Friedman, D. Koller, A. Pfeffer, and B. Taskar. Probabilistic relational models. In An Introduction to Statistical Relational Learning. MIT Press, 2007.Google ScholarCross Ref
- L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning probabilistic models of link structure. JMLR, 3:679--707, 2002. Google ScholarDigital Library
- L. Getoor and D. Jensen, editors. Learning Statistical Models from Relational Data at AAAI 2000, Austin, TX, 2000.Google Scholar
- L. Getoor and L. Mihalkova. Exploiting statistical & relational information on the web and in social media: Applications, techniques, and new frontiers, 2010. AAAI Tutorial.Google Scholar
- L. Getoor and B. Taskar, editors. Introduction to Statistical Relational Learning. MIT Press, Cambridge, MA, 2007. Google ScholarDigital Library
- J. Halpern. An analysis of first-order logics for reasoning about probability. Artificial Intelligence, 46:311--350, 1990. Google ScholarDigital Library
- M. Jaeger. Relational bayesian networks. In UAI, 1997.Google Scholar
- A. Jaimovich, O. Meshi, and N. Friedman. Template based inference in symmetric relational Markov random fields. In UAI, 2007.Google Scholar
- K. Kersting. SRL without tears: A gentle introduction to SRL, 2008. ILP Tutorial. Google ScholarDigital Library
- K. Kersting, B. Ahmadi, and S. Natarajan. Counting belief propagation. In UAI, 2009. Google ScholarDigital Library
- K. Kersting and L. De Raedt. Bayesian logic programs. Technical report, Freiburg, 2001. Google ScholarDigital Library
- K. Kersting, S. Russell, L. P. Kaelbling, A. Halevy, S. Natarajan, and L. Mihalkova, editors. AAAI-10 Workshop on Statistical Relational AI, Atlanta, GA, 2010.Google Scholar
- D. Koller and A. Pfeffer. Probabilistic frame-based systems. In AAAI, 1998. Google ScholarDigital Library
- N. Lavrauc and S. D\uzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994. Google ScholarDigital Library
- Q. Lu and L. Getoor. Link-based classification. In ICML, 2003.Google ScholarDigital Library
- B. Milch, L. S. Zettlemoyer, K. Kersting, M. Haimes, and L. P. Kaelbling. Lifted probabilistic inference with counting formulas. In AAAI, 2008. Google ScholarDigital Library
- S. Muggleton. Stochastic logic programs. In New Generation Computing. Academic Press, 1996.Google Scholar
- S. H. Muggleton, editor. Inductive Logic Programming. Academic Press, New York, NY, 1992.Google Scholar
- J. Neville and D. Jensen. Iterative classification in relational data. In SRL, 2000.Google Scholar
- J. Neville and F. Provost. Social network mining, 2008. AAAI Tutorial.Google Scholar
- D. Poole. First-order probabilistic inference. In IJCAI, 2003. Google ScholarDigital Library
- M. Richardson and P. Domingos. Markov logic networks. Machine Learning, 62:107--136, 2006. Google ScholarDigital Library
- P. Sen, A. Deshpande, and L. Getoor. Exploiting shared correlations in probabilistic databases. In VLDB, 2008. Google ScholarDigital Library
- P. Sen, A. Deshpande, and L. Getoor. Bisimulation-based approximate lifted inference. In UAI, 2009. Google ScholarDigital Library
- P. Sen, G. M. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93--106, 2008.Google ScholarDigital Library
- P. Singla and P. Domingos. Lifted first-order belief propagation. In AAAI, 2008. Google ScholarDigital Library
Index Terms
- Learning statistical models from relational data
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