Introduction to statistical relational learning

By Lise Getoor, B. Taskar. 2007

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Citations in the corpus

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)).”
Unsupervised Ontology Induction from Text (2010, background): “Combining uncertainty handling and joint inference is the hallmark of the emerging field of statistical relational learning (a.k.a. structured prediction), where a plethora of approaches have been developed (Getoor and Taskar, 2007; Bakir et al., 2007).”
KnowRob: A knowledge processing infrastructure for cognition-enabled robots (2013, background): “There are various logical languages for describing first-order relations, ranging from generic predicate logics (Frege, 1879) over logical programming languages such as Prolog (Sterling and Shapiro, 1994) to languages that combine logical with probabilistic representations (Getoor and Taskar, 2007).”
Scalable Learning of Collective Behavior (2012, background): “In the same process, social media provides ample opportunities to study human interactions and collective behavior on an unprecedented scale.”
Knowledge Graph Completion via Complex Tensor Factorization (2017, methodology): “The need for high quality predictions required by link prediction applications made it progressively become the main problem in statistical relational learning (Getoor and Taskar, 2007), a research field interested in relational data representation and modeling.”
Fairness in Relational Domains (2018, methodology): “To overcome the limitations of attribute-based fairness, we introduce a new statistical relational learning (SRL) framework [14] suitable for modelling fairness in relational domain.”
Practical probabilistic programming with monads (2015, background): “A lot of work has been put into combining graphical models with ﬁrst-order logic (Getoor and Taskar 2007), which extends the set of representable models.”