The automatic extraction of relations from unstructured natural text is challenging but offers practical solutions for many problems like automatic text understanding and semantic retrieval. Relation extraction can be formulated as a classification problem using support vector machines and kernels for structured data that may include parse trees to account for syntactic structure. In this paper we present new tree kernels over dependency parse trees automatically generated from natural language text. Experiments on a public benchmark data set show that our kernels with richer structural features significantly outperform all published approaches for kernel-based relation extraction from dependency trees. In addition we optimize kernel computations to improve the actual runtime compared to previous solutions.