Papers

A full set of working notes is available here

Papers accepted to the workshop are below, in alphabetical order of author. Please add comments to the paper comment pages and please reply to comments on your own paper. If you add a comment, please consider mailing the author to alert the authors.

Speeding up multi-relational data mining
Anna Atramentov and Vasant Honavar
Comments

The relational vector-space model and industry classification
Abraham Bernstein, Scott Clearwater, and Foster Provost
Comments

Categorizing unsupervised relational learning algorithms
Hannah Blau and Amy McGovern
Comments

Aggregation versus selection bias, and relational neural networks
Hendrik Blockeel and Maurice Bruynooghe
Comments

Feature extraction languages for propositionalized relational learning
Chad Cumby and Dan Roth
Comments

Individuals, relations and structures in probabilistic models
James Cussens
Comments | Mail author

Ecosystem analysis using probabilistic relational modeling
Bruce D'Ambrosio, Eric Altendorf, and Jane Jorgensen
Comments

Research on statistical relational learning at the University of Washington
Pedro Domingos, Yeuhi Abe, Corin Anderson, Anhai Doan, Dieter Fox, Alon Halevy, Geoff Hulten, Henry Kautz, Tessa Lau, Lin Liao, Jayant Madhavan, Mausam, Donald J. Patterson, Matthew Richardson, Sumit Sanghai, Daniel Weld and Steve Wolfman
Comments

Relational Learning for Securities Market Regulation
Henry Goldberg
Comments | Mail author

Social Network Relational Vectors for Anonymous Identity Matching
Shawndra Hill
Comments

Mining massive relational databases
Geoff Hulten, Pedro Domingos, and Yeuhi Abe
Comments

Representational power of probabilistic-logical models: From upgrading to downgrading
Kristian Kersting
Comments | Mail author

Logical Markov decision programs
Kristian Kersting and Luc De Raedt
Comments | Mail authors

First-order probabilistic models for information extraction
Bhaskara Marthi, Brian Milch, and Stuart Russell
Comments | Mail authors

A Note on the Unification of Information Extraction and Data Mining using Conditional-Probability, Relational Models
Andrew McCallum and David Jensen
Comments | Mail authors

The Variable Precision Rough Set Inductive Logic Programming model -- a statistical relational learning perspective
R. Milton, V. Maheswari and A. Siromoney
Comments | Mail authors

Statistical relational learning: Four claims and a survey
Jennifer Neville, Matthew Rattigan, and David Jensen
Comments | Mail authors

Parameter estimation for stochastic context-free graph grammars
Tim Oates, Fang Huang, and Shailesh Doshi
Comments

Aggregation and concept complexity in relational learning
Claudia Perlich and Foster Provost
Comments

Aggregation-based feature invention and relational concept classes (supporting paper)
Claudia Perlich and Foster Provost
Comments

Relational learning problems and simple models
Foster Provost, Claudia Perlich and Sofus Macskassy
Comments

Statistical relational learning for link prediction
Alexandrin Popescul and Lyle H. Ungar
Comments

A comparison of Stochastic Logic Programs and Bayesian Logic Programs
Aymeric Puech and Stephen Muggleton
Comments | Mail author

Why the title of this workshop should be "Learning relational statistical models from data"
Stuart Russell
Comments | Mail author

Learning statistical models of time-varying relational data
Sumit Sanghai, Pedro Domingos and Daniel Weld
Comments

A new perspective of statistical modeling with PRISM
Taisuke Sato and Neng-Fu Zhou
Comments

Relational learning: A web-page classification viewpoint
Sean Slattery
Comments

Statistical modeling of graph and network data
Padhraic Smyth
Comments

Label and link prediction in relational data
Ben Taskar, Pieter Abbeel, Ming-Fai Wong, and Daphne Koller
Comments

Toward a high-performance system for symbolic and statistical modeling
Neng-Fa Zhou, Taisuke Sato, and Koiti Hasidad
Comments