Abstract This paper describes a framework for building story traces (compact global views of a narrative) and story projections (selections of key elements of a narrative) and their applications in text understanding and classification. Word and sense properties are extracted from documents using the WordNet lexical database enhanced with Prolog inference rules and a number of lexical transformations. Inference rules are based on navigation in various WordNet relation chains (hypernyms, meronyms, entailment and causality links, etc.) and derived relations expressed as Boolean combinations of node and edge properties used to direct the navigation. The resulting abstract story traces provide a compact view of the underlying narrative's key content elements and a means for automated indexing and classification of text collections. Ontology driven projections act as a kind of “semantic lenses” and provide a means to select a subset of a narrative whose key sense elements are subsumed by a set of concepts, predicates and properties expressing the focus of interest of a user. Finally, we discuss applications of these techniques in text understanding, classification of text collections and answering questions about a text.