The tools to analyze and visualize information from multiple, heterogeneous sources have often relied on innovations in statistical methods. The results from purely statistical methods, however, overlook relevant semantic features present within natural language and text-based information. Emerging research in ontology languages (e.g. RDF, RDFS, SUO-KIF, and OWL) offers promising avenues for overcoming these limitations by leveraging existing and future libraries of meta-data and semantic mark-up. Using semantic features (e.g. hypernyms, meronyms, synonyms, etc.) encoded in ontology languages, methods such as keyword search and clustering can be augmented to analyze and visualize documents at conceptually richer levels. We present findings from a hierarchical clustering system modified for ontological indexing and run on a topic-centric test collection of documents each with fewer than 200 words. Our findings show that ontologies can impose a complete interpretation or subjective clustering onto a document set that is at least as good as meta-word search.