Learning to Extract Relations for Relational Classification
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Relational classification aims at including relations among entities into the classification process, for example taking relations among documents such as common authors or citations into account. However, considering more than one relation can further improve classification accuracy. Here we introduce a new approach to make use of several relations as well as both, relations and local attributes for classification using ensemble methods. To accomplish this, we present a generic relational ensemble model that can use different relational and local classifiers as components. Furthermore, we discuss solutions for several problems concerning relational data such as heterogeneity, sparsity, and multiple relations. Especially the sparsity problem will be discussed in more detail. We introduce a new method called PRNMultiHop that tries to handle this problem. Furthermore we categorize relational methods in a systematic way. Finally, we provide empirical evidence, that our relational ensemble methods outperform existing relational classification methods, even rather complex models such as relational probability trees (RPTs), relational dependency networks (RDNs) and relational Bayesian classifiers (RBCs).