Statistical Relational Learning for Document Mining

A major obstacle to fully integrated deployment of manydata mining algorithms is the assumption that data sitsin a single table, even though most real-world databaseshave complex relational structures. We propose an integratedapproach to statistical modeling from relationaldatabases. We structure the search space based on "refinementgraphs", which are widely used in inductive logic programmingfor learning logic descriptions. The use of statisticsallows us to extend the search space to include richerset of features, including many which are not boolean.Search and model selection are integrated into a single process,allowing information criteria native to the statisticalmodel, for example logistic regression, to make feature selectiondecisions in a step-wise manner. We present experimentalresults for the task of predicting where scientific paperswill be published based on relational data taken fromCiteSeer. Our approach results in classification accuraciessuperior to those achieved when using classical "flat" features.The resulting classifier can be used to recommendwhere to publish articles.