2003 StatisticalRelLearnForDocMining

• (Popescul et al., 2003) ⇒ Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock. (2003). “Statistical Relational Learning for Document Mining.” In: Proceedings of ICDM Conference (ICDM 2003). doi:10.1109/ICDM.2003.1250930

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~41 http://scholar.google.com/scholar?cites=14036812468418431538&as_sdt=2000

Quotes

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 ininductive 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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 StatisticalRelLearnForDocMining	Alexandrin Popescul Lyle H. Ungar Steve Lawrence David M. Pennock			Statistical Relational Learning for Document Mining		Proceedings of ICDM Conference	http://www.cis.upenn.edu/~popescul/Publications/popescul03dm.pdf	10.1109/ICDM.2003.1250930		2003