Statistical Relational Learning Algorithm
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A Statistical Relational Learning Algorithm is a relational learning algorithm that is a statistical learning algorithm (which relies on statistical theory).
- Context:
- It can handle Non-IID Samples.
- It can be applied by a Statistical Relational Learning System (to solve a statistical relational learning task).
- It can make use of a Statistical Relational Language.
- It can be implemented by a Statistical Relational Learning System (to solve a statistical relational learning task).
- It can make use of a Relational Feature, such as a Node-centric Feature (such as Node Aggregation Feature, or a Relation-centric Feature.
- Example(s):
- Counter-Example(s):
- See: Lifted Inference Algorithm.
References
2017
- (De Raedt et al., 2017) ⇒ Luc De Raedt, Kristian Kersting, Sriraam Natarajan, and David Poole. (2017). “Statistical Relational Artificial Intelligence Tutorial at AAAI-2017"
- QUOTE: Markov Logic Networks, Problog, (Exact) Lifted Inference, Representation Issues, Propositional Inference, Approximate Lifted Inference, ...
2016a
- (De Raedt et al., 2016) ⇒ Luc De Raedt, Kristian Kersting, Sriraam Natarajan, and David Poole. (2016). “Statistical Relational Artificial Intelligence: Logic, Probability, and Computation." Morgan and Claypool Publishers. ISBN: 9781627058414
2016b
- (Nickel et al., 2016) ⇒ Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. (2016). “A Review of Relational Machine Learning for Knowledge Graphs.” In: Proceedings of the IEEE, 104(1). doi:10.1109/JPROC.2015.2483592
- QUOTE: Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. This could be class labels, a regression score, or an unsupervised cluster id or latent vector (embedding). In statistical relational learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labeled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. These tasks arise in many settings such as analysis of social networks and biological pathways. For further information on SRL, see (De Raedt, 2008; Getoor & Taskar, 2007; Džeroski & Lavrač, 2001).
2012
- http://en.wikipedia.org/wiki/Statistical_relational_learning
- Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with models of domains that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with reasoning (specifically probabilistic inference) and knowledge representation. Therefore, alternative terms that reflect the main foci of the field include statistical relational learning and reasoning (emphasizing the importance of reasoning) and first-order probabilistic languages (emphasizing the key properties of the languages with which models are represented).
- Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with models of domains that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
2008
- (De Raedt, 2008) ⇒ L. De Raedt. (2008). “Logical and Relational Learning." Springer-Verlag.
2007
- (Getoor & Taskar, 2007) ⇒ Lise Getoor, and Ben Taskar, editors. (2007). “Introduction to Statistical Relational Learning." MIT Press. ISBN:0262072882.
- QUOTE: 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.
2001
- (Džeroski & Lavrač, 2001) ⇒ S. Džeroski, and N. Lavrač. (2001). “Relational Data Mining". Springer-Verlag.