2003 StatisticalRelLearnForLinkPred

From GM-RKB
Jump to: navigation, search

Subject Headings: Link Prediction Algorithm, Inductive Logic Programming.

Notes

Cited By

Quotes

Abstract

Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting citations made in scientific literature using relational data taken from CiteSeer. This data includes the citation graph, authorship and publication venues of papers, as well as their word content.


References

,

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2003 StatisticalRelLearnForLinkPredAlexandrin Popescul
Lyle H. Ungar
Statistical Relational Learning for Link PredictionProceedings of IJCAI Workshop on Learning Statistical Models from Relational Datahttp://www.cis.upenn.edu/~popescul/Publications/statistical03link.pdf2003