  Data augmentation uses artificially-created examples to support supervised
machine learning, adding robustness to the resulting models and helping to
account for limited availability of labelled data. We apply and evaluate a
synthetic data approach to relationship classification in digital libraries,
generating artificial books with relationships that are common in digital
libraries but not easier inferred from existing metadata. We find that for
classification on whole-part relationships between books, synthetic data
improves a deep neural network classifier by 91%. Further, we consider the
ability of synthetic data to learn a useful new text relationship class from
fully artificial training data.
