Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.