PropBank has been widely used as training data for Semantic Role Labeling. However, because this training data is taken from the WSJ, the resulting machine learning models tend to overfit on idiosyncrasies of that text's style, and do not port well to other genres. In addition, since PropBank was designed on a verb-by-verb basis, the argument labels Arg2 - Arg5 get used for very diverse argument roles with inconsistent training instances. For example, the verb ``make'' uses Arg2 for the ``Material'' argument; but the verb ``multiply'' uses Arg2 for the ``Extent'' argument. As a result, it can be difficult for automatic classifiers to learn to distinguish arguments Arg2-Arg5. We have created a mapping between PropBank and VerbNet what provides a VerbNet thematic role label for each verb-specific PropBank label. Since VerbNet uses argument labels that are more consistent across verbs, we are able to demonstrate that these new labels are easier to learn. This talk will describe PropBank and VerbNet, and the mapping between them, and present the resulting improvements in automatic Semantic Role Labeling. Preliminary mappings to FrameNet and CYC will also be described.