Learning statistical models from relational data

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.

[1]  Lise Getoor,et al.  Probabilistic Similarity Logic , 2010, UAI.

[2]  S. Muggleton.  Stochastic Logic Programs , 1996 .

[3]  Ben Taskar,et al.  Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) , 2007 .

[4]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[5]  Lise Getoor.  Tutorial on Statistical Relational Learning , 2005, ILP.

[6]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming , 2004, ALT.

[7]  Ben Taskar,et al.  Learning Probabilistic Models of Relational Structure , 2001, ICML.

[8]  Jennifer Neville,et al.  Iterative Classification in Relational Data , 2000 .

[9]  Ofer Meshi,et al.  Template Based Inference in Symmetric Relational Markov Random Fields , 2007, UAI.

[10]  Kristian Kersting.  SRL without Tears: An ILP Perspective , 2008, ILP.

[11]  Dan Roth,et al.  Lifted First-Order Probabilistic Inference , 2005, IJCAI.

[12]  Vítor Santos Costa,et al.  Inductive Logic Programming , 2013, Lecture Notes in Computer Science.

[13]  Ben Taskar,et al.  Learning Probabilistic Models of Link Structure , 2003, J. Mach. Learn. Res..

[14]  Lise Getoor,et al.  Bisimulation-based Approximate Lifted Inference , 2009, UAI.

[15]  Avi Pfeffer,et al.  Probabilistic Frame-Based Systems , 1998, AAAI/IAAI.

[16]  Ben Taskar,et al.  Selectivity estimation using probabilistic models , 2001, SIGMOD '01.

[17]  Pedro M. Domingos,et al.  Markov Logic: An Interface Layer for Artificial Intelligence , 2009, Markov Logic: An Interface Layer for Artificial Intelligence.

[18]  Ben Taskar,et al.  Probabilistic Models of Text and Link Structure for Hypertext Classification , 2001 .

[19]  Saso Dzeroski,et al.  Inductive Logic Programming: Techniques and Applications , 1993 .

[20]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..

[21]  Charu C. Aggarwal,et al.  Managing and Mining Graph Data , 2010, Managing and Mining Graph Data.

[22]  Lise Getoor,et al.  From Instances to Classes in Probabilistic Relational Models , 2000, ICML 2000.

[23]  Lise Getoor,et al.  Exploiting shared correlations in probabilistic databases , 2008, Proc. VLDB Endow..

[24]  Pedro M. Domingos,et al.  Lifted First-Order Belief Propagation , 2008, AAAI.

[25]  Luc De Raedt,et al.  Logical and relational learning , 2008, Cognitive Technologies.

[26]  Luc De Raedt,et al.  Bayesian Logic Programs , 2001, ILP Work-in-progress reports.

[27]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[28]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[29]  Kristian Kersting,et al.  Counting Belief Propagation , 2009, UAI.

[30]  Joseph Y. Halpern.  An Analysis of First-Order Logics of Probability , 1989, IJCAI.

[31]  Leslie Pack Kaelbling,et al.  Lifted Probabilistic Inference with Counting Formulas , 2008, AAAI.

[32]  Dan Roth,et al.  MPE and Partial Inversion in Lifted Probabilistic Variable Elimination , 2006, AAAI.

[33]  Manfred Jaeger,et al.  Relational Bayesian Networks , 1997, UAI.

[34]  J. Davenport.  Editor , 1960 .

[35]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming , 2004, Probabilistic Inductive Logic Programming.

[36]  Ben Taskar,et al.  Probabilistic Relational Models , 2014, Encyclopedia of Social Network Analysis and Mining.

[37]  David Poole,et al.  First-order probabilistic inference , 2003, IJCAI.

[38]  Thomas Hofmann,et al.  Predicting Structured Data (Neural Information Processing) , 2007 .

[39]  Gökhan BakIr,et al.  Predicting Structured Data , 2008 .