  Information extraction can support novel and effective access paths for
digital libraries. Nevertheless, designing reliable extraction workflows can be
cost-intensive in practice. On the one hand, suitable extraction methods rely
on domain-specific training data. On the other hand, unsupervised and open
extraction methods usually produce not-canonicalized extraction results. This
paper tackles the question how digital libraries can handle such extractions
and if their quality is sufficient in practice. We focus on unsupervised
extraction workflows by analyzing them in case studies in the domains of
encyclopedias (Wikipedia), pharmacy and political sciences. We report on
opportunities and limitations. Finally we discuss best practices for
unsupervised extraction workflows.
