A Kernel Function for Redundant Features from RDF Graphs and Its Fast Calculation

Machine learning on RDF data has become important in the field of the Semantic Web. However, RDF graph structures are redundantly represented by noisy and incomplete data on theWeb. In order to apply SVMs to such RDF data, we propose a kernel function to compute the similarity between resources on RDF graphs. This kernel function is defined by selected features on RDF paths that eliminate the redundancy on RDF graphs with information gain ratio filtering. Kernel functions are a very flexible framework and cannot be applied to only SVMs but also principal component analysis, canonical correlation analysis, clustering and so on. However, the calculation of the proposed kernel function requires high costs for time and memory due to the exponential increase of features in RDF graphs. Therefore, we propose an efficient algorithm that calculates the kernel for redundant features from RDF graphs. Our experiments show the performance of the proposed kernel with SVMs on classification tasks for RDF resources and the advantages over existing kernels.