Relational Kernel Machines for Learning from Graph-Structured RDF Data

Despite the increased awareness that exploiting the large amount of semantic data requires statistics-based inference capabilities, only little work can be found on this direction in the Semantic Web research. On semantic data, supervised approaches, particularly kernel-based Support Vector Machines (SVM), are promising. However, obtaining the right features to be used in kernels is an open problem because the amount of features that can be extracted from the complex structure of semantic data might be very large. Further, combining several kernels can help to deal with efficiency and data sparsity but creates the additional challenge of identifying and joining different subsets of features or kernels, respectively. In this work, we solve these two problems by employing the strategy of dynamic feature construction to compute a hypothesis, representing the relevant features for a set of examples. Then, a composite kernel is obtained from a set of clause kernels derived from components of the hypothesis. The learning of the hypothesis and kernel(s) is performed in an interleaving fashion. Based on experiments on real-world datasets, we show that the resulting relational kernel machine improves the SVM baseline.