A Kernel Function for Redundant Features from RDF Graphs

Abstract

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 the Web. 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. Our experiments show the performance of the proposed kernel with SVMs on binary classification tasks for RDF resources.