Abstract

Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best described by relational models in which instances of multiple types are related to each other in complex ways. For example, in a scientific paper domain, papers are related to each other via citation, and are also related to their authors. In this case, the label of one entity (e.g., the topic of the paper) is often correlated with the labels of related entities. We propose a general class of models for classification and clustering in relational domains that capture probabilistic dependencies between related instances. We show how to learn such models efficiently from data. We present empirical results on two real world data sets. Our experiments in a transductive classification setting indicate that accuracy can be significantly improved by modeling relational dependencies. Our algorithm automatically induces a very natural behavior, where our knowledge about one instance helps us classify related ones, which in turn help us classify others. In an unsupervised setting, our models produced coherent clusters with a very natural interpretation, even for instance types that do not have any attributes. 1

Citations

6232	Maximum likelihood from incomplete data via the em algorithm - Dempster, Laird, et al. - 1977
5665	Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference - Pearl - 1988
2221	Authoritative Sources in a Hyperlinked Environment - Kleinberg - 1998
1484	D: Pattern Classification - Duda, Hart, et al.
421	Learning probabilistic relational models - Friedman, Getoor, et al. - 1999
416	Bayesian classification (AutoClass): Theory and results - Cheeseman, Stutz - 1996
364	Loopy belief propagation for approximate inference: An empirical study - Murphy, Weiss, et al. - 1999
189	The Bayesian Structural EM Algorithm - Friedman - 1998
168	Probabilistic frame-based systems - Koller, Pfeffer - 1998
159	The missing link - a probabilistic model of document content and hypertext connectivity - Cohn, Hofmann
141	Automating the construction of internet portals with machine learning - AK, Nigam, et al.
128	Latent class models for collaborative filtering - Hofmann, Puzicha - 1999
109	Learning to probabilistically identify authoritative documents - Cohn, Chang - 2000
98	Iterative classification in relational data - Neville, Jensen - 2000
48	Discovering test set regularities in relational domains - Slattery, Mitchell - 2000
2	Combining statistical and relational methods in hypertext domains - Slattery, Craven - 1998