This paper examines the consequences of looking at digital libraries where the desired unit of interpretation is smaller than a text: it is a keyword, a motif, or a metaphor. In volume organizations, keywords and metaphors are low-common denominators. At the semantic level—the level of words, images, and metaphors—long-term regularity and patterns emerge in collection, analysis, and taxonomy. The paper develops through three stages: first, the manual curation of a high-quality database of metaphors; the expansion of this database through automated and human-assisted techniques. Finally, possible future experiments and opportunities for the application of machine learning, data mining, and natural language processing techniques to help find patterns and meaning concealed at this important level of granularity are described.