Statistical Relational Learning for Document Mining

Loading...
Thumbnail Image
Penn collection
Departmental Papers (CIS)
Degree type
Discipline
Subject
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Lawrence, Steve
Pennock, David M.
Contributor
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.

Advisor
Date of presentation
2003-11-19
Conference name
Departmental Papers (CIS)
Conference dates
2023-05-16T21:39:40.000
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Copyright 2003 IEEE. Reprinted from Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), pages 275-282. Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=27998&page=2 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Recommended citation
Collection