Introduction to Statistical Relational Learning

By Charles Sutton, Andrew McCallum. 2007

1,270 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Probabilistic & Bayesian Methods

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Citations in the corpus

DeepWalk: online learning of social representations (2014, background): “Machine learning applications in networks (such as network classification [15, 37], content rec1 2”
The Graph Neural Network Model (2009, neutral): “Those techniques include random fields [42], Bayesian networks [43], statistical relational learning [44], transductive learning [45], and semisupervised approaches for graph processing [46].”
DeepProbLog: Neural Probabilistic Logic Programming (2018, background): “Indeed, today there exist approaches that tightly integrate logical and probabilistic reasoning with statistical learning; cf. the areas of statistical relational artiﬁcial intelligence [De Raedt et al., 2016, Getoor and Taskar, 2007] and probabilistic logic programming [De Raedt and Kimmig, 2015].”
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “This encouraged the design of concept languages for PGMs, culminating in the area of statistical relational learning [Getoor and Taskar, 2007].”
Relational inductive biases, deep learning, and graph networks (2018, background): “, 2001) and statistical relational learning (Getoor and Taskar, 2007).”
Fitted Q-Learning for Relational Domains (2020, methodology): “We employ Functional Gradient Boosting (Friedman 2001) in relational domains (Getoor 2007; De Raedt et al. 2016) to capture the Bellman error for every Q-learning iteration and obtain lifted basis functions in a non-parametric fashion.”
Representation Learning on Graphs: Methods and Applications (2017, background): “More generally, link prediction is closely related to statistical relational learning [24], where a common task is to predict missing relations between entities in a knowledge graph [42].”
Practical Recommendations for Gradient-Based Training of Deep Architectures (2012, methodology): “, 2011), Markov Logic Networks (Richardson and Domingos, 2006) and relational learning (Getoor and Taskar, 2006).”
End-to-end Differentiable Proving (2017, background): “We refer the reader to [27, 28] for a more in-depth introduction.”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “Traditional statistical learning approaches (Getoor & Taskar, 2007) such as Markov-logic networks (Richard-son & Domingos, 2006) usually suffer from scalability issues.”
Holographic Embeddings of Knowledge Graphs (2015, background): “Hence, learning from and with relational knowledge representations has long been considered an important task in artiﬁcial intelligence (see e.g., Getoor and Taskar (2007); Muggleton (1991); Gentner (1983); Kemp et al. (2006); Xu et al. (2006); Richardson and Domingos (2006)).”
A neural joint model for entity and relation extraction from biomedical text (2017, background): “Roth and Yih [16] proposed a joint inference framework based on integer linear programming to extract entities and relations.”
Relational retrieval using a combination of path-constrained random walks (2010, background): “There is an interesting connection between the Relational Retrieval (RR) problems considered in this work and Statistical Relational Learning (SRL) problems (Getoor and Taskar 2007).”
Learning Mixtures of Submodular Shells with Application to Document Summarization (2012, background): “Structured prediction problems [51, 50, 14] are those where these domains may consist of combinatorial structures.”
Non-parametric policy gradients: a unified treatment of propositional and relational domains (2008, background): “Second, a small change in the value-function parameter can push the value of one action over that of another, causing a discontinuous change in the policy, the states visited, and overall performance.”