A short introduction to probabilistic soft logic

By Angelika Kimmig, Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor. 2012

245 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Neural Logic Machines (2019, background): “Being a powerful way of reasoning over discrete symbols, it is successfully applied to various language-related problems, and has been integrated into modern learning frameworks (Kerst-ing et al., 2000; Richardson & Domingos, 2006; Kimmig et al., 2012).”
Integrating Learning and Reasoning with Deep Logic Models (2019, background): “On the other hand, symbolic reasoning [7, 16, 23], which is typically based on logical and probabilistic inference, allows to perform high-level reasoning (possibly under uncertainty) without having to deal with thousands of learning hyper-parameters.”
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “and probabilistic soft logic (Kimmig et al. 2012).”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “Probabilistic soft logic (PSL) [131] provides a convenient form of ‘‘syntactic sugar’’ for defining HLMRFs, just as MLNs provide a form of syntactic sugar for regular (boolean) MRFs.”
Inherent Disagreements in Human Textual Inferences (2019, background): “Such work includes (controversial) formalisms such as fuzzy set theory (Zadeh, 1994, 1996), as well as more generally accepted formalisms which assume access to boolean groundings, such as probabilistic soft logic (Friedman et al., 1999; Kimmig et al., 2012; Beltagy et al., 2014) and Markov logic ne”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, background): “contain rich background information and have been widely studied in knowledge acquisition and inference, usually on the basis of Markov logic networks [40], [41], [42].”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, unknown): “Examples of such models include [191, 57, 121, 116].”
A Logic-Driven Framework for Consistency of Neural Models (2019, background): “The Łukasiewicz t-norm drives knowledge-driven learning and inference in probabilistic soft logic (Kimmig et al., 2012).”
Knowledge Graph Identification (2013, background): “Probabilistic soft logic (PSL) [7, 14] is a recently-introduced framework which allows users to specify rich probabilistic models over continuous-valued random variables.”
Diachronic Embedding for Temporal Knowledge Graph Completion (2019, background): “StaRAI: Statistical relational AI (StaRAI) [50, 31] approaches are mainly based on soft (hanf-crafted or learned) rules [51, 11, 29, 26] where the probability of a world is typically proportional to the number of rules that are satisfied/violated in that world and the confidence for each rule.”
CLN2INV: Learning Loop Invariants with Continuous Logic Networks (2019, background): “Several recent works use differentiable logic to learn boolean logic formulas from noisy data (Kimmig et al., 2012; Evans & Grefenstette, 2018; Payani & Fekri, 2019) or improving adversarial robustness by applying logical rules to training”
On a Convex Logic Fragment for Learning and Reasoning (2018, methodology): “PSL assumes the assignment of a template to each clause c j , that is reused for each single grounding of the clause in the interpretation.”
A Semantic Loss Function for Deep Learning with Symbolic Knowledge (2017, methodology): “(2016), using a simple extension of Probabilistic Soft Logic (PSL) (Kimmig et al., 2012).”
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015, methodology): “Any declarative factor graph framework (e.g., Alchemy [1], DeepDive [2, 35], PSL [5]) can by used.”
Structure learning for relational logistic regression: an ensemble approach (2018, background): “They have generally ranged from directed models (Kersting and De Raedt 2007; Koller 1999; Heckerman, Meek, and Koller 2007; Kazemi et al. 2014a; Neville and Jensen 2007; Kazemi and Poole 2018) to undirected models (Richardson and Domingos 2006; Taskar et al. 2007; Kimmig et al. 2012).”
Zero-shot Learning of Classifiers from Natural Language Quantification (2018, background): “Similarly, Probabilistic Soft Logic (Kimmig et al., 2012) allows users to specify rules in a logical language that can be used for reasoning over graphical models.”
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints (2020, background): “Thus, they provide a framework to exploit the relational structure of different problems [Kimmig et al. 2012].”