Robust mechanisms for ontology selection are crucial for the evolving Semantic Web characterized by rapidly increasing numbers of online ontologies and by applications that automatically use the associated metadata. However, existing selection techniques have primarily been designed in the context of human mediated tasks and fall short of supporting automatic knowledge reuse. We address this gap by proposing a selection algorithm that takes into account 1) the needs of two applications that explore large scale, distributed markup and 2) some properties of online ontology repositories. We conclude that the ambitious context of automatic knowledge reuse imposes several challenging requirements on selection.