This paper describes Stanford's work on the Self-Archiving Legacy Toolkit (SALT), which is exploring the capability of a human-computer system to transform unstructured and heterogeneous data into navigable collections of information presented in context. Stanford's University Archives are beginning to digitize and present the collected "papers" of luminary faculty. The full potential of this resource of scientific legacy is not realized by simple digitization. Through the application of semantic processing technologies (coupled with rich visualization tools), a luminary's lifetime collection of research, publications, correspondence and presentations can be accessed not only by keyword, but also by concept, collaborators, time, place, organization and even project. Our hypothesis is that these facets will transform the processing and delivery of personal archival collections, and expose the historical context and intellectual concepts threading through the careers of some of the greatest scientists and thinkers of the 20th century.

Central to the vision of SALT is the notion of self-description of a luminary's own corpus. In addition to providing oral history, video commentary and textual annotation to their collected works, the toolkit gives eminent researchers the tools to create, apply and edit their own taxonomies, ontologies and controlled vocabularies to their works. These knowledge editing tools will enable the archival subject to efficiently identify people, places, times, and concepts mentioned in their collected works and materials, and to draw relationships among these items to reveal paths of influence and the historic progression of ideas. In this way, they can interpret and extend their collection with their own personal viewpoint, creating a uniquely personal presentation of their own life story, which augments rather than replaces the traditional privileged view of archival provenance.

We are testing these tools with Edward Feigenbaum, a luminary AI scientist, whose collected papers have been partly digitized and analyzed. This paper will present findings, demonstrate the tool chain in its current prototype state, and outline areas for future effort.