Streamlining the Production and Visualization of Error-Free Geospatially Grounded Text Documents

We will streamline the production of ‘self-mapping’ documents through the tight integration of evidence-driven automated tagging with a mixed-initiative approach to tag validation and editing. GeoDoc will deviate substantially from the traditional corpus driven approaches to metadata generation in order to address the unique issues involved in producing error-free geospatially grounded documents. In particular, we will enhance automated tagging accuracy by combining a new model of evidence integration with a wide range of operationally-validated heuristic evidence sources. The design of GeoDoc is predicated on the fact that, within this task, the ultimate measure of system efficacy is the level of human-effort required to produce error-free documents. GeoDoc will support a new cooperative model of editing wherein the system orders editing decisions so as to reduce overall uncertainty in its tagging and thereby reduce document validation time. Documents produced with GeoDoc will support new modes of visualization (and other services), offering substantial value to the information consumer. In this Phase II we will build on the strong foundation established in Phase I, and work with our partner, ESRI to construct and field an operational geotext publishing and visualization capability and proceed toward successful commercialization.