Published Articles >> Table of Contents >> Abstract

Third IEEE International Conference on Data Mining (ICDM'03)   p. 275
Statistical Relational Learning for Document Mining
Alexandrin Popescul, University of Pennsylvania, Philadelphia
Lyle H. Ungar, University of Pennsylvania, Philadelphia
Steve Lawrence, Google, Mountain View, CA
David M. Pennock, Overture Services, Inc., Pasadena, CA

Full Article Text:

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250930
Send link to a friend

Abstract
A major obstacle to fully integrated deployment of many data mining algorithms is the assumption that data sits in a single table, even though most real-world databases have complex relational structures. We propose an integrated approach to statistical modeling from relational databases. We structure the search space based on "refinement graphs", which are widely used in inductive logic programming for learning logic descriptions. The use of statistics allows us to extend the search space to include richer set of features, including many which are not boolean. Search and model selection are integrated into a single process, allowing information criteria native to the statistical model, for example logistic regression, to make feature selection decisions in a step-wise manner. We present experimental results for the task of predicting where scientific papers will be published based on relational data taken from CiteSeer. Our approach results in classification accuracies superior to those achieved when using classical "flat" features. The resulting classifier can be used to recommend where to publish articles.
Back to Top Additional Information
Citation:  Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock, "Statistical Relational Learning for Document Mining," icdm, p. 275,  Third IEEE International Conference on Data Mining (ICDM'03),  2003