Statistical relational learning: Four claims and a survey
J. Neville, M. Rattigan, and D. Jensen. Proceedings of the Workshop on Learning Statistical Models from Relational Data, Eighteenth International Joint Conference on Artificial Intelligence, (2003)
Statistical relational learning (SRL) research has made significant progress over the last 5 years. We have successfully demonstrated the feasibility of a number of probabilistic models for relational data, including probabilistic relational models, Bayesian logic programs, and relational probability trees, and the interest in SRL isgrowing. However, in order to sustain and nurture the growth of SRL as a subfield we need to refocus our efforts on the science of machine learning — moving from demonstrations to comparative and ablation studies. We will outline four assertions that are implicit to SRL research but which have been only minimally evaluated. We hope to stimulate discussion as to how, as a community, these claims can be addressed in future research.