Application of Statistical Relational Learning to Hybrid Recommendation Systems Shuo Yang* and Mohammed Korayem and Khalifeh AlJadda and Trey Grainger and Sriraam Natarajan* * School of Informatics and Computing, Indiana

Source URL: arxiv.org

Statistical Relational Learning for Proteomics: Function, Interactions, Evolution Stefano TesoInternational Doctorate School in Information and Communication Technologies

Source URL: eprints-phd.biblio.unitn.it

Inductive Logic Programming meets Relational Databases: An Application to Statistical Relational Learning Marcin Malec, Tushar Khot, James Nagy, Erik Blasch, and Sriraam Natarajan Abstract With the increasing amount of r

Source URL: homes.soic.indiana.edu

A Short Introduction to Probabilistic Soft Logic Angelika Kimmig1,2 , Stephen H. Bach1 , Matthias Broecheler3 , Bert Huang1 , Lise Getoor1 1 University of Maryland, 2 KU Leuven, 3 Aurelius LLC

Source URL: stephenbach.net

• Logic
• Philosophy
• Abstraction
• Bayesian statistics
• Markov networks
• Reasoning
• Model theory
• Semantics
• Probabilistic soft logic
• Markov logic network
• Probabilistic logic
• Statistical relational learning

Statistical Relational Learning with Soft Quantifiers Golnoosh Farnadi1,2 , Stephen H. Bach3 , Marjon Blondeel4 , Marie-Francine Moens2 , Lise Getoor5 , and Martine De Cock1,6 1 Dept. of Applied Mathematics, Computer Sc

Source URL: stephenbach.net

• Logic
• Mathematical logic
• Abstraction
• Quantification
• Predicate logic
• Model theory
• Semantics
• Formal languages
• Quantifier
• First-order logic
• Constructible universe
• Universal quantification

Learning Symmetric Relational Markov Random Fields A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science by

Source URL: www.cs.huji.ac.il

• Graphical models
• Statistics
• Probability
• Statistical theory
• Variable elimination
• Markov random field
• Markov chain
• Belief propagation
• Conceptual model
• Bayesian network
• Deep learning

ABSTRACT Title of dissertation: HINGE-LOSS MARKOV RANDOM FIELDS AND PROBABILISTIC SOFT LOGIC:

Source URL: stephenbach.net

• Statistics
• Machine learning
• Probability
• Statistical models
• Graphical models
• Bayesian statistics
• Markov networks
• Statistical relational learning
• Probabilistic soft logic
• Markov random field
• Structured prediction
• Expectationmaximization algorithm

Probabilistic Similarity Logic Matthias Br¨ocheler Computer Science Dept. University of Maryland College Park, MD 20740

Source URL: psl.umiacs.umd.edu

• Geometry
• Knowledge engineering
• Information science
• Information
• Knowledge representation
• Ontology
• Semantic Web
• PSL
• Cosine similarity
• Statistical relational learning
• Similarity
• Analogy

A Short Introduction to Probabilistic Soft Logic Angelika Kimmig1,2 , Stephen H. Bach1 , Matthias Broecheler3 , Bert Huang1 , Lise Getoor1 1 University of Maryland, 2 KU Leuven, 3 Aurelius LLC

Source URL: psl.umiacs.umd.edu

• Logic
• Philosophy
• Bayesian statistics
• Markov networks
• Model theory
• Reasoning
• Semantics
• Probabilistic soft logic
• Markov logic network
• Probabilistic logic
• Statistical relational learning
• Logical truth

Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning Matthias Br¨ocheler, Lise Getoor University of Maryland, College Park College Park, MD 20742

Source URL: psl.umiacs.umd.edu

• Operations research
• Summary statistics
• Statistical models
• Convex optimization
• Linear programming
• Normal distribution
• Probability distribution
• Central limit theorem
• Expected value
• Random walk
• Stochastic process
• Standard deviation