Understanding Data Through Analysis of Structured Markup and Usage

Data and documents with semantic markup (such as in XML) allows for the construction of usage models that are human interpretable. A framework for performing analysis over semantic markup data in conjunction with usage information is described along with applications for performing automated clustering and “find-similar”. A strong emphasis on “human interpretability” is placed on the results of these analytical techniques.