The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Introduction to Statistical Relational Learning
Abstract
Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
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Cites
(1988) · Cited by 16,582
Richardson, M., & Domingos, P. (2006) · Machine Learning · doi:10.1007/s10994-006-5833-1 · Cited by 2,676
Learning Probabilistic Relational Models
Getoor, L. C., Friedman, N., Koller, D., & Pfeffer, A. (2001) · Springer eBooks · doi:10.1007/978-3-662-04599-2_13 · Cited by 936
An analysis of first-order logics of probability
Halpern, J. Y. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90019-v · Cited by 588
Probabilistic Horn abduction and Bayesian networks
Poole, D. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90061-f · Cited by 581
First-order probabilistic inference
Poole, D. (2003) · Cited by 410
Lifted First-Order Probabilistic Inference
Braz, R. D. S., Amir, E., & Roth, D. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7432.003.0017 · Cited by 274
Bayesian Logic ProgramsPreprint
Kersting, K., & Raedt, L. D. (2001) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0111058 · Cited by 166
CLP(BN): Constraint Logic Programming for Probabilistic KnowledgePreprint
Costa, V. S., Page, D., Qazi, M., & Cussens, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1212.2519 · Cited by 96
Probabilistic logic programming and Bayesian networks
Ngo, L., & Haddawy, P. (1995) · Lecture notes in computer science · doi:10.1007/3-540-60688-2_51 · Cited by 60
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Sorted by citations
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutiérrez, C., Kirrane, S., Gayo, J. E. L., Roberto Navigli, A., Neumaier, S., et al. (2021) · ACM Computing Surveys · doi:10.1145/3447772 · Cited by 2,823
Practical Recommendations for Gradient-Based Training of Deep Architectures
Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259
A Three-Way Model for Collective Learning on Multi-Relational Data
Nickel, M., Tresp, V., & Kriegel, H. (2011) · Cited by 1,806
Metadata
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2007-08-31
- OpenAlex
- W1585529040
- Cited by
- 1,643
- References
- 11
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 36 · 2023: 42 · 2022: 41 · 2021: 67 · 2020: 100 · 2019: 86 · 2018: 77 · 2017: 115 · 2016: 94 · 2015: 116 · 2014: 125 · 2013: 132 · 2012: 129
- FWCI
- 22.83
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- The MIT Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Constraint Satisfaction and Optimization
- Keywords
- statistical relational learning, probabilistic relational models, Markov logic, information extraction, probabilistic graphical models, knowledge representation, uncertainty reasoning, object-oriented model
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