Recent work on graphical models for relational data has demonstrated significant improvements in classification and inference when models represent the dependencies among instances. Despite its use in conventional statistical models, the assumption of instance independence is contradicted by most relational data sets. For example, in citation data there are dependencies among the topics of a paper's references, and in genomic data there are dependencies among the functions of interacting proteins. In this paper, we present relational dependency networks (RDNs), graphical models that are capable of expressing and reasoning with such dependencies in a relational setting. We discuss RDNs in the context of relational Bayes networks and relational Markov networks and outline the relative strengths of RDNs-namely, the ability to represent cyclic dependencies, simple methods for parameter estimation, and efficient structure learning techniques. The strengths of RDNs are due to the use of pseudolikelihood learning techniques, which estimate an efficient approximation of the full joint distribution. We present learned RDNs for a number of real-world data sets and evaluate the models in a prediction context, showing that RDNs identify and exploit cyclic relational dependencies to achieve significant performance gains over conventional conditional models. In addition, we use synthetic data to explore model performance under various relational data characteristics, showing that RDN learning and inference techniques are accurate over a wide range of conditions. Keywords: relational learning, probabilistic relational models, knowledge discovery, graphical models, dependency networks, pseudolikelihood estimation 2. Another class of joint models extend conventional logic programming models to support probabilistic reasoning in first-order logic environments (Kersting and Raedt, 2002; Richardson and Domingos, 2006) . We refer to these models as probabilistic logic models (PLMs). See Section 5.2 for more detail. 3. We use the term relational Bayesian network to refer to Bayesian networks that have been upgraded to model relational databases. The term has also been used by Jaeger (1997) to refer to Bayesian networks where the nodes correspond to relations and their values represent possible interpretations of those relations in a specific domain. 4. The limitation is due to the PRM modeling approach (see Section 3.
As a solution to the cyclicity problem, Neville and Jensen proposed upgrading Dependency Networks for relational data REF .
Relational dependency networks
Journal of Machine Learning Research
Computer Science